[
  {
    "title": "Microstructure & physicochemical properties dataset of NaCl-based salt mixtures for concentrating solar power",
    "doi": "10.1038/s41597-025-06437-z",
    "url": "https://doi.org/10.1038/s41597-025-06437-z",
    "journal": "Scientific Data",
    "year": 2026,
    "authors": "Feng, Y.; Wu, Y.; Wang, W.",
    "abstract": "Abstract\n                  Concentrating solar power is a pivotal technology in global transition toward renewable energy, providing a viable pathway for dispatchable and base-load electricity generation. An important component of the concentrating solar power system is molten salts, particularly NaCl-based mixtures, which serve as both efficient heat transfer fluids and high-capacity thermal energy storage media. The influence mechanisms of micro-ionic interactions and microstructure on physico",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "Novel Low/Zero Carbon Technologies",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Global 0.05° Grid-Based Dataset of Keyhole Imagery with Spatio-Temporal Indicators (1960–1984)",
    "doi": "10.1038/s41597-026-06866-4",
    "url": "https://doi.org/10.1038/s41597-026-06866-4",
    "journal": "Scientific Data",
    "year": 2026,
    "authors": "Wang, T.; Zhang, X.; Shan, M.; Deng, M.; Wang, J.",
    "abstract": "Abstract\n                  The American satellite reconnaissance program (Keyhole imagery) is serving as a significant data source for geoscience research because of its high-resolution and early temporal coverage, while lack of spatial and temporal description of its uneven distribution could hinder researchers from selecting/accessing appropriate the Keyhole images. Here we introduce a global grid–based dataset that organizes declassified U.S. Keyhole imagery (1960–1984) for direct reuse, buil",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Bounding the costs of electric vehicle managed charging—supply curves for scenarios from 2025 to 2050",
    "doi": "10.1038/s41597-026-07008-6",
    "url": "https://doi.org/10.1038/s41597-026-07008-6",
    "journal": "Scientific Data",
    "year": 2026,
    "authors": "Matsuda-Dunn, R.; Hale, E.; Estreich, E.; Lavin, L.; Konar-Steenberg, G.",
    "abstract": "Abstract\n                  As electric vehicle (EV) adoption increases, the resulting EV battery charging will increase demand on the electric power grid. Through EV managed charging (EVMC) programs, charging can be shifted in time to support electric grid reliability and reduce electricity costs. EVMC can offer an alternative to additional supply-side generation, but the costs of EVMC implementation must be understood to evaluate the cost-benefits of EVMC. This paper presents bottom-up, forward",
    "data_url": "",
    "source": "CrossRef",
    "direction": "FLEXERGY",
    "subcategory": "Electric Vehicles & Mobility",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Co-crystal engineering unlocks high-stability perovskite solar modules",
    "doi": "10.1038/s41560-025-01904-8",
    "url": "https://doi.org/10.1038/s41560-025-01904-8",
    "journal": "Nature Energy",
    "year": 2026,
    "authors": "",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "CleanTech",
    "subcategory": "Solar PV & Storage",
    "direction_label": "Novel Low/Zero Carbon Technologies",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Negative pricing increases electricity use but challenges grid stability",
    "doi": "10.1038/s41560-025-01928-0",
    "url": "https://doi.org/10.1038/s41560-025-01928-0",
    "journal": "Nature Energy",
    "year": 2026,
    "authors": "",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "The integration imperative in electricity grid transition",
    "doi": "10.1038/s41560-025-01915-5",
    "url": "https://doi.org/10.1038/s41560-025-01915-5",
    "journal": "Nature Energy",
    "year": 2026,
    "authors": "O’Malley, M.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Global gridded dataset of heating and cooling degree days under climate change scenarios",
    "doi": "10.1038/s41893-025-01754-y",
    "url": "https://doi.org/10.1038/s41893-025-01754-y",
    "journal": "Nature Sustainability",
    "year": 2026,
    "authors": "Lizana, J.; Miranda, N.; Sparrow, S.; Wallom, D.; Khosla, R.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Probabilistic day-ahead forecasting of system-level renewable energy and electricity demand",
    "doi": "10.1038/s41467-026-69015-w",
    "url": "https://doi.org/10.1038/s41467-026-69015-w",
    "journal": "Nature Communications",
    "year": 2026,
    "authors": "Terrén-Serrano, G.; Deshmukh, R.; Martínez-Ramón, M.",
    "abstract": "Abstract\n                  Increasing shares of wind and solar generation, together with rising electricity demand, introduce growing uncertainty into power system operations. Accurate day-ahead forecasts of electricity demand and renewable generation are essential for system operators to coordinate electricity markets and maintain reliability at low cost. Here, we show that forecasting based on joint probability distributions of demand and renewable supply can substantially improve system-level",
    "data_url": "",
    "source": "CrossRef",
    "direction": "CleanTech",
    "subcategory": "Solar PV & Storage",
    "direction_label": "Carbon Trading & New Business Models",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Behavioral uncertainty in EV charging drives heterogeneous grid load variability under climate goals",
    "doi": "10.1038/s41467-025-66796-4",
    "url": "https://doi.org/10.1038/s41467-025-66796-4",
    "journal": "Nature Communications",
    "year": 2026,
    "authors": "Zhang, B.; Xin, Q.; Chen, S.; Wang, Z.; Lu, Y.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "FLEXERGY",
    "subcategory": "Electric Vehicles & Mobility",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Reconstructing fine-scale 3D wind fields with terrain-informed machine learning",
    "doi": "10.1038/s41467-026-70562-5",
    "url": "https://doi.org/10.1038/s41467-026-70562-5",
    "journal": "Nature Communications",
    "year": 2026,
    "authors": "Lin, C.; Tie, R.; Yi, S.; Liu, D.; Zhong, X.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "AI & Deep Learning"
  },
  {
    "title": "Energy-efficient wireless sensor network for urban groundwater level monitoring using machine learning and sink mobility",
    "doi": "10.1038/s41598-026-39435-1",
    "url": "https://doi.org/10.1038/s41598-026-39435-1",
    "journal": "Scientific Reports",
    "year": 2026,
    "authors": "Manchanda, R.; Lakshmi, A.; Kaur, G.; Sudhamsu, G.; Samal, S.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "AI & Deep Learning"
  },
  {
    "title": "Predicting energy prices and renewable energy adoption through an optimized tree-based learning framework with explainable artificial intelligence",
    "doi": "10.1038/s41598-026-35706-z",
    "url": "https://doi.org/10.1038/s41598-026-35706-z",
    "journal": "Scientific Reports",
    "year": 2026,
    "authors": "Tang, T.",
    "abstract": "Abstract\n                  This research offers a comprehensive analysis of global energy consumption, focusing on predicting two key metrics: the Energy Price Index and the Renewable Energy Share. The study employs advanced Machine Learning (ML) regression techniques, all further optimized using metaheuristic algorithms. In addition, a primary objective of this study is to determine which variables most significantly affect model performance and predictive accuracy. Through SHAP (SHapley Additi",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "AI & Deep Learning"
  },
  {
    "title": "Innovative fuzzy reinforcement learning based energy management for smart homes through optimization of renewable energy resources with starfish optimization algorithm",
    "doi": "10.1038/s41598-026-40247-6",
    "url": "https://doi.org/10.1038/s41598-026-40247-6",
    "journal": "Scientific Reports",
    "year": 2026,
    "authors": "Hamedani, M.; Jahangiri, A.; Mehri, R.; Shamim, A.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Comparative evaluation of several models for forecasting hourly electricity use in a steel plant",
    "doi": "10.1038/s41598-026-43868-z",
    "url": "https://doi.org/10.1038/s41598-026-43868-z",
    "journal": "Scientific Reports",
    "year": 2026,
    "authors": "Gu, F.; Zhao, Y.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "A collaborative multi-party encryption for mitigating man-in-the-middle attacks in smart grid and energy IoT systems",
    "doi": "10.1038/s41598-026-43856-3",
    "url": "https://doi.org/10.1038/s41598-026-43856-3",
    "journal": "Scientific Reports",
    "year": 2026,
    "authors": "Alfawair, M.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "The peak shifting electricity consumption management and influencing factors of smart grid from recurrent neural network model and deep learning",
    "doi": "10.1038/s41598-026-35754-5",
    "url": "https://doi.org/10.1038/s41598-026-35754-5",
    "journal": "Scientific Reports",
    "year": 2026,
    "authors": "Wang, F.; Huang, D.; Lu, W.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Quantum-driven frequency stability in Indian prospect smart grid with electric vehicle charging station integration and real-time hardware validation",
    "doi": "10.1038/s41598-025-32156-x",
    "url": "https://doi.org/10.1038/s41598-025-32156-x",
    "journal": "Scientific Reports",
    "year": 2026,
    "authors": "Kaleeswari, M.; Sivakumar, P.; Aswini, A.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "FLEXERGY",
    "subcategory": "Electric Vehicles & Mobility",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "A multi strategy optimization framework using AI digital twins for smart grid carbon emission reduction",
    "doi": "10.1038/s41598-026-38720-3",
    "url": "https://doi.org/10.1038/s41598-026-38720-3",
    "journal": "Scientific Reports",
    "year": 2026,
    "authors": "Sakthivel, S.; Arivukarasi, M.; Charulatha, G.; Nithisha, J.; Abirami, B.",
    "abstract": "Abstract\n                  This research presents an AI-enabled digital twin framework to achieve carbon neutrality in smart grids through optimal management of heterogeneous energy storage systems. The proposed structure integrates battery, thermal, and hydrogen storage technologies with AI-driven forecasting models to address the challenge of renewable integration, while maintaining grid stability and economic viability. This paper presents a comparative analysis of three distinct optimization",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Electric vehicle charging station recommendation system based on graph neural network and context-aware refinement",
    "doi": "10.1038/s41598-026-41271-2",
    "url": "https://doi.org/10.1038/s41598-026-41271-2",
    "journal": "Scientific Reports",
    "year": 2026,
    "authors": "Seo, D.; Moon, J.; Kwon, H.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "FLEXERGY",
    "subcategory": "Electric Vehicles & Mobility",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "AI & Deep Learning"
  },
  {
    "title": "A multi-dimensional feature aggregation network for electric vehicle charging demand prediction",
    "doi": "10.1038/s41598-026-38855-3",
    "url": "https://doi.org/10.1038/s41598-026-38855-3",
    "journal": "Scientific Reports",
    "year": 2026,
    "authors": "Yu, Y.; He, L.; Yu, Z.; Tu, Y.; Jing, X.",
    "abstract": "Abstract\n                  \n                    Accurate prediction of urban electric vehicle (EV) charging demand is critical for infrastructure planning and dynamic pricing strategies. Although various methods have been developed, most existing studies focus primarily on spatiotemporal dependencies, paying limited attention to interactions among multivariate features. Furthermore, conventional serial spatiotemporal architectures typically extract features dimension-by-dimension, which may impe",
    "data_url": "",
    "source": "CrossRef",
    "direction": "FLEXERGY",
    "subcategory": "Electric Vehicles & Mobility",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Optimized scheduling of integrated energy systems considering waste-to-power plants and advanced adiabatic air compression energy storage machines",
    "doi": "10.1038/s41598-026-37485-z",
    "url": "https://doi.org/10.1038/s41598-026-37485-z",
    "journal": "Scientific Reports",
    "year": 2026,
    "authors": "Wang, W.; Liu, M.; Zhao, H.; Wu, Y.; Tian, Y.",
    "abstract": "Abstract\n                  To achieve carbon peaking and carbon neutrality goals, improve energy utilization efficiency, and accelerate the decarbonization of energy structure, this paper proposes a model that integrates Waste Incineration Power Plant (WIP) and Advanced Adiabatic Compressed Air Energy Storage (AA-CAES) to reduce carbon emissions and enhance system economics. First, based on the coupled WIP and Power-to-Gas (P2G) model, a waste heat recovery unit is introduced to recover exhaust ",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Energy consumption forecasting in logistics considering environmental and operational constraints using FT-transformer architecture",
    "doi": "10.1038/s41598-025-34414-4",
    "url": "https://doi.org/10.1038/s41598-025-34414-4",
    "journal": "Scientific Reports",
    "year": 2026,
    "authors": "Yan, L.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Minimization of outage probability and energy consumption by deep learning-based prediction in D2D mm wave communication",
    "doi": "10.1038/s41598-025-34846-y",
    "url": "https://doi.org/10.1038/s41598-025-34846-y",
    "journal": "Scientific Reports",
    "year": 2026,
    "authors": "Bilal, N.; Velmurugan, T.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "A probabilistic framework for effective battery energy storage sizing in microgrids with demand response",
    "doi": "10.1038/s41598-026-35145-w",
    "url": "https://doi.org/10.1038/s41598-026-35145-w",
    "journal": "Scientific Reports",
    "year": 2026,
    "authors": "Alamir, N.; Kamel, S.; Megahed, T.; Hori, M.; Abdelkader, S.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "FLEXERGY",
    "subcategory": "Demand Response",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Optimized economic scheduling of demand response in integrated energy systems considering dynamic energy efficiency and dynamic carbon trading",
    "doi": "10.1038/s41598-025-33497-3",
    "url": "https://doi.org/10.1038/s41598-025-33497-3",
    "journal": "Scientific Reports",
    "year": 2026,
    "authors": "Mao, H.; Deng, Q.; Zhang, Z.; Yang, X.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "EnergiTrade",
    "subcategory": "Energy & Carbon Trading",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Renewable-powered high-temperature compressed air energy storage to accelerate grid decarbonization",
    "doi": "10.1016/j.crsus.2026.100639",
    "url": "https://doi.org/10.1016/j.crsus.2026.100639",
    "journal": "Cell Reports Sustainability",
    "year": 2026,
    "authors": "Yang, D.; Wang, J.; Tang, G.; He, W.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Inventory Optimization under Tri Phased Demand with Dual Aging and Controlled Backlogging",
    "doi": "10.1016/j.isci.2026.115268",
    "url": "https://doi.org/10.1016/j.isci.2026.115268",
    "journal": "iScience",
    "year": 2026,
    "authors": "E, A.; S, U.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Electroluminescent perovskite QD–based neural networks for energy-efficient and accelerate multitasking learning",
    "doi": "10.1126/sciadv.ady8518",
    "url": "https://doi.org/10.1126/sciadv.ady8518",
    "journal": "Science Advances",
    "year": 2026,
    "authors": "Park, Y.; Wang, G.",
    "abstract": "\n                    The ability of multitasking (MT) learning in neuro-inspired artificial intelligence (AI) systems offers promise for energy-efficient deployment in robotics, health care, and autonomous vehicles. Here, an MT learning framework is established using a dual-output electroluminescent synaptic device array based on a mixed-dimensional stacked configuration with Cs\n                    \n                      1−\n                      x\n                    \n                    FA\n    ",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "AI & Deep Learning"
  },
  {
    "title": "On-demand cancer immunotherapy via single-cell encapsulation of synthetic circuit–engineered cells",
    "doi": "10.1126/sciadv.aea3573",
    "url": "https://doi.org/10.1126/sciadv.aea3573",
    "journal": "Science Advances",
    "year": 2026,
    "authors": "Zhao, Y.; Li, R.; Han, Y.; Shi, C.; Lee, K.",
    "abstract": "\n                    Despite the therapeutic potential of engineered immune cell therapy against metastases, it faces challenges including cytokine-driven systemic toxicity, off-target biodistribution, and host rejection. Here, we develop red/far-red light-regulated individually encapsulated (RL/FRL-EnE) cells, integrating optogenetics with biomaterial encapsulation for precise immunomodulation. This system uses a phytochrome A–based photoswitch (ΔPhyA-PCB) that enables bidirectional control. RL",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Programmable electric tweezers",
    "doi": "10.1126/sciadv.aec3443",
    "url": "https://doi.org/10.1126/sciadv.aec3443",
    "journal": "Science Advances",
    "year": 2026,
    "authors": "Chen, Y.; Tan, H.; Zhuang, J.; Xu, Y.; Zhang, C.",
    "abstract": "The interaction between a single microscopic object such as a cell or a molecule and electromagnetic field is fundamental in single-object manipulation such as optical trap and magnetic trap. Function-on-demand, single-object manipulation requires local high-freedom control of electromagnetic field, which remains challenging. Here, we propose a manipulation concept: programmable single-object manipulation, based on programming the electromagnetic field in a multibit electrode system realized on ",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "UrbanEV: An Open Benchmark Dataset for Urban Electric Vehicle Charging Demand Prediction",
    "doi": "10.1038/s41597-025-04874-4",
    "url": "https://doi.org/10.1038/s41597-025-04874-4",
    "journal": "Scientific Data",
    "year": 2025,
    "authors": "Li, H.; Qu, H.; Tan, X.; You, L.; Zhu, R.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "FLEXERGY",
    "subcategory": "Electric Vehicles & Mobility",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "CPVPD-2024: A Chinese photovoltaic plant dataset derived via a topography-enhanced deep learning framework",
    "doi": "10.1038/s41597-025-05891-z",
    "url": "https://doi.org/10.1038/s41597-025-05891-z",
    "journal": "Scientific Data",
    "year": 2025,
    "authors": "Yang, Y.; Lin, S.; Lu, R.; Liu, X.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "CleanTech",
    "subcategory": "Solar PV & Storage",
    "direction_label": "Novel Low/Zero Carbon Technologies",
    "refined_category": "AI & Deep Learning"
  },
  {
    "title": "Longitudinal Dataset of Net-load, PV Production and Solar Irradiation from Madeira Island, Portugal",
    "doi": "10.1038/s41597-025-06118-x",
    "url": "https://doi.org/10.1038/s41597-025-06118-x",
    "journal": "Scientific Data",
    "year": 2025,
    "authors": "Pereira, L.; Monteiro, D.; Apina, F.; Scuri, S.; Barreto, M.",
    "abstract": "Abstract\n                  This paper presents the PTProsumer dataset, a high-resolution dataset of photovoltaic (PV) production and net-load measurements collected from 24 prosumers - entities that both produce and consume electricity, including households and small commercial buildings - on Madeira Island, Portugal. The dataset covers monitoring periods ranging from 3 months to 5 years, with measurements sampled at a 1-second resolution, resulting in approximately 3.89 billion data points. PV ",
    "data_url": "",
    "source": "CrossRef",
    "direction": "CleanTech",
    "subcategory": "Solar PV & Storage",
    "direction_label": "Novel Low/Zero Carbon Technologies",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "A Real-World Energy Management Dataset from a Smart Company Building for Optimization and Machine Learning",
    "doi": "10.1038/s41597-025-05186-3",
    "url": "https://doi.org/10.1038/s41597-025-05186-3",
    "journal": "Scientific Data",
    "year": 2025,
    "authors": "Engel, J.; Castellani, A.; Wollstadt, P.; Lanfermann, F.; Schmitt, T.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "China’s product-level CO2 emissions dataset aligned with national input-output tables from 1997 to 2020",
    "doi": "10.1038/s41597-025-04366-5",
    "url": "https://doi.org/10.1038/s41597-025-04366-5",
    "journal": "Scientific Data",
    "year": 2025,
    "authors": "Li, X.; Liu, Y.; Zhang, J.; Zhou, M.; Meng, B.",
    "abstract": "AbstractCarbon emission research based on input-output tables (IOTs) has received attention, but data quality issues persist due to inconsistencies between the sectoral scopes of energy statistics and IOTs. Specifically, China’s official energy data are reported at the industry level, whereas IOTs are organized by product sectors. Valid IOT-based environmental models require consistent transformation from industry-level to product-level emissions. However, most existing studies overlook this nec",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Demand Response & IoT"
  },
  {
    "title": "A hierarchical dataset on multiple energy consumption and PV generation with emissions and weather information",
    "doi": "10.1038/s41597-025-06010-8",
    "url": "https://doi.org/10.1038/s41597-025-06010-8",
    "journal": "Scientific Data",
    "year": 2025,
    "authors": "Dong, H.; Zhu, J.; Chung, C.; Liang, Z.; Yang, H.",
    "abstract": "Abstract\n                  This study constructs a multi-source and hierarchical dataset of energy consumption, photovoltaic (PV) power generation, greenhouse gas (GHG) emissions, and weather information, dubbed Hierarchical Energy, Emissions, and Weather (HEEW). This dataset contains 11,987,328 records for 147 individual buildings, four aggregated communities, and the entire region, which is structured as time-series tables indexed by building ID and timestamps from 1 January 2014 to 31 Decembe",
    "data_url": "",
    "source": "CrossRef",
    "direction": "CleanTech",
    "subcategory": "Solar PV & Storage",
    "direction_label": "Novel Low/Zero Carbon Technologies",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "COFACTOR Drammen dataset - 4 years of hourly energy use data from 45 public buildings in Drammen, Norway",
    "doi": "10.1038/s41597-025-04708-3",
    "url": "https://doi.org/10.1038/s41597-025-04708-3",
    "journal": "Scientific Data",
    "year": 2025,
    "authors": "Lien, S.; Walnum, H.; Sørensen, Å.",
    "abstract": "Abstract\n          To limit energy consumption and peak loads with increased electrification of our society, more information is needed about the energy use in buildings. This article presents a data set that contains 4 years (Jan. 2018- Dec. 2021/Mar. 2022) of hourly measurements of energy and weather data from 45 public buildings located in Drammen, Norway. The buildings are schools (16), kindergartens (20), nursing homes (7) and offices (2). For each building, the data set contains contextual",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "5G High Density Demand Dataset in Liverpool City Region, UK",
    "doi": "10.1038/s41597-025-06282-0",
    "url": "https://doi.org/10.1038/s41597-025-06282-0",
    "journal": "Scientific Data",
    "year": 2025,
    "authors": "Maheshwari, M.; Raschellà, A.; Mackay, M.; Eiza, M.; Wetherall, J.",
    "abstract": "Abstract\n                  The wireless network data are a feasible way to understand the user behavior in a given environment and may be utilized for analysis, prediction and optimization. On the other hand, datasets from wireless service providers are not publicly available, and obtaining a dataset in real time is challenging. In this work, we present a 5G dense deployment dataset obtained from the Liverpool City Region High Density Demand (LCR HDD) project. The project involves network deploy",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "Carbon Trading & New Business Models",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "A 20-year dataset (2001–2020) of global cropland water-use efficiency at 1-km grid resolution",
    "doi": "10.1038/s41597-025-04904-1",
    "url": "https://doi.org/10.1038/s41597-025-04904-1",
    "journal": "Scientific Data",
    "year": 2025,
    "authors": "Jiang, M.; Zheng, C.; Jia, L.; Chen, J.",
    "abstract": "Abstract\n          Cropland water-use efficiency (WUE) is an essential indicator for the sustainable utilization of agricultural water resources. The lack of long-term global cropland WUE datasets with high spatial resolution limits our understanding of global and regional patterns of cropland WUE. This study developed a long-term global cropland WUE dataset at 1-km spatial resolution from 2001 to 2020. The cropland WUE was obtained as the ratio between net primary productivity (NPP) and evapotr",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "Carbon Trading & New Business Models",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Underground well water level observation grid dataset from 2005 to 2022",
    "doi": "10.1038/s41597-025-04799-y",
    "url": "https://doi.org/10.1038/s41597-025-04799-y",
    "journal": "Scientific Data",
    "year": 2025,
    "authors": "Wang, M.; Yao, J.; Chang, H.; Liu, R.; Xu, N.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Dataset of CO2 geological storage potential and injection rate capacity in China based on fine grid technology",
    "doi": "10.1038/s41597-025-04875-3",
    "url": "https://doi.org/10.1038/s41597-025-04875-3",
    "journal": "Scientific Data",
    "year": 2025,
    "authors": "Fan, J.; Xiang, X.; Yao, Y.; Li, K.; Li, Z.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "HIPGDAC-ES: historical population grid data compilation for Spain (1900–2021)",
    "doi": "10.1038/s41597-025-04533-8",
    "url": "https://doi.org/10.1038/s41597-025-04533-8",
    "journal": "Scientific Data",
    "year": 2025,
    "authors": "Goerlich, F.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "Novel Low/Zero Carbon Technologies",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "High-temporal-resolution dataset of uni-, bidirectional, and dynamic electric vehicle charging profiles",
    "doi": "10.1038/s41597-025-05524-5",
    "url": "https://doi.org/10.1038/s41597-025-05524-5",
    "journal": "Scientific Data",
    "year": 2025,
    "authors": "Esser, M.; Orfanoudakis, S.; Homaee, O.; Vahidinasab, V.; Vergara, P.",
    "abstract": "Abstract\n          The transition to Electric Vehicles (EVs) introduces challenges for power grid integration, particularly due to the growing demand for charging infrastructure. To support research on smart charging strategies and bidirectional charging applications, this study presents an open-access dataset containing 142 EV charging profiles obtained in a laboratory environment. The dataset includes static charging and discharging scenarios alongside dynamic profiles where the charging power",
    "data_url": "",
    "source": "CrossRef",
    "direction": "FLEXERGY",
    "subcategory": "Electric Vehicles & Mobility",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Unveiling Energy Dynamics of Battery Electric Vehicle Using High-Resolution Data",
    "doi": "10.1038/s41597-025-06148-5",
    "url": "https://doi.org/10.1038/s41597-025-06148-5",
    "journal": "Scientific Data",
    "year": 2025,
    "authors": "Yasko, M.; Moussa Issaka, A.; Tian, F.; Kazmi, H.; Driesen, J.",
    "abstract": "Abstract\n                  Battery electric vehicles (BEVs) have increasingly positioned themselves as a critical technology in the power system, impacting the world’s energy consumption. Understanding the BEV energy dynamics can contribute to vehicle, infrastructure, and grid optimization. Currently, BEV manufacturers provide limited access to the vehicle’s high energy consuming components, such as the battery and the charger. Therefore, existing public datasets consist mostly of aggregated dat",
    "data_url": "",
    "source": "CrossRef",
    "direction": "FLEXERGY",
    "subcategory": "Electric Vehicles & Mobility",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "High-resolution gridded dataset of China’s offshore wind potential and costs under technical change",
    "doi": "10.1038/s41597-025-04428-8",
    "url": "https://doi.org/10.1038/s41597-025-04428-8",
    "journal": "Scientific Data",
    "year": 2025,
    "authors": "An, K.; Cai, W.; Lu, X.; Wang, C.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Renewable Energy Resource Mapping",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Power price stability and the insurance value of renewable technologies",
    "doi": "10.1038/s41560-025-01704-0",
    "url": "https://doi.org/10.1038/s41560-025-01704-0",
    "journal": "Nature Energy",
    "year": 2025,
    "authors": "Navia Simon, D.; Diaz Anadon, L.",
    "abstract": "Abstract\n          To understand if renewables stabilize or destabilize electricity prices, we simulate European power markets as projected by the National Energy and Climate Plans for 2030 but replicating the historical variability in electricity demand, the prices of fossil fuels and weather. We propose a β-sensitivity metric, defined as the projected increase in the average annual price of electricity when the price of natural gas increases by 1 euro. We show that annual power prices spikes w",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Grid-scale corrosion-free Zn/Br flow batteries enabled by a multi-electron transfer reaction",
    "doi": "10.1038/s41560-025-01907-5",
    "url": "https://doi.org/10.1038/s41560-025-01907-5",
    "journal": "Nature Energy",
    "year": 2025,
    "authors": "Xu, Y.; Li, T.; Peng, Z.; Xie, C.; Li, X.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "AI data centres as grid-interactive assets",
    "doi": "10.1038/s41560-025-01927-1",
    "url": "https://doi.org/10.1038/s41560-025-01927-1",
    "journal": "Nature Energy",
    "year": 2025,
    "authors": "Colangelo, P.; Coskun, A.; Megrue, J.; Roberts, C.; Sengupta, S.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "Carbon Trading & New Business Models",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Spatiotemporal assessment of renewable adequacy during diverse extreme weather events in China",
    "doi": "10.1038/s41467-025-60264-9",
    "url": "https://doi.org/10.1038/s41467-025-60264-9",
    "journal": "Nature Communications",
    "year": 2025,
    "authors": "Jiang, K.; Liu, N.; Wang, K.; Chen, Y.; Wang, J.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Efficiency optimization for large-scale droplet-based electricity generator arrays with integrated microsupercapacitor arrays",
    "doi": "10.1038/s41467-025-64289-y",
    "url": "https://doi.org/10.1038/s41467-025-64289-y",
    "journal": "Nature Communications",
    "year": 2025,
    "authors": "Li, Z.; Chen, S.; Fu, Y.; Li, J.",
    "abstract": "Abstract\n          Droplet-based electricity generators are lightweight and nearly metal-free, making them promising for hydraulic power applications. However, two critical challenges hinder their practical application: significant performance degradation, potentially up to 90%, in existing small-scale integrated panels, and low efficiency, often less than 2%, in storing the irregular high-voltage pulsed electricity produced by large-scale arrays. Here, we demonstrate that by tailoring the botto",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Speed modulations in grid cell information geometry",
    "doi": "10.1038/s41467-025-62856-x",
    "url": "https://doi.org/10.1038/s41467-025-62856-x",
    "journal": "Nature Communications",
    "year": 2025,
    "authors": "Ye, Z.; Wessel, R.",
    "abstract": "Abstract\n                  Grid cells, with hexagonal spatial firing patterns, are thought critical to the brain’s spatial representation. High-speed movement challenges accurate localization as self-location constantly changes. Previous studies of speed modulation focus on individual grid cells, yet population-level noise covariance can significantly impact information coding. Here, we introduce a Gaussian Process with Kernel Regression (GKR) method to study neural population representation geo",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Grid congestion stymies climate benefit from U.S. vehicle electrification",
    "doi": "10.1038/s41467-025-61976-8",
    "url": "https://doi.org/10.1038/s41467-025-61976-8",
    "journal": "Nature Communications",
    "year": 2025,
    "authors": "Duan, C.; Motter, A.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "China’s urban EV ultra-fast charging distorts regulated price signals and elevates risk to grid stability",
    "doi": "10.1038/s41467-025-63199-3",
    "url": "https://doi.org/10.1038/s41467-025-63199-3",
    "journal": "Nature Communications",
    "year": 2025,
    "authors": "Yu, Q.; Zhao, P.; Li, J.; Wang, H.; Yan, J.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Entorhinal grid-like codes for visual space during memory formation",
    "doi": "10.1038/s41467-025-64307-z",
    "url": "https://doi.org/10.1038/s41467-025-64307-z",
    "journal": "Nature Communications",
    "year": 2025,
    "authors": "Graichen, L.; Linder, M.; Keuter, L.; Jensen, O.; Doeller, C.",
    "abstract": "Abstract\n                  Eye movements, such as saccades, allow us to gather information about the environment and, in this way, can shape memory. In non-human primates, saccades are associated with the activity of grid cells in the entorhinal cortex. Grid cells are essential for spatial navigation, but whether saccade-based grid-like signals play a role in human memory formation is currently unclear. Here, human participants undergo functional magnetic resonance imaging and continuous eye gaz",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Planning the electric vehicle transition by integrating spatial information and social networks",
    "doi": "10.1038/s41467-025-66072-5",
    "url": "https://doi.org/10.1038/s41467-025-66072-5",
    "journal": "Nature Communications",
    "year": 2025,
    "authors": "Wu, J.; Salgado, A.; González, M.",
    "abstract": "Abstract\n                  The transition from gasoline-powered vehicles to plug-in electric vehicles (PEVs) offers a promising pathway for reducing greenhouse gas emissions. Spatial forecasts of PEV adoption are essential to support power grid adaptation, yet forecasting is hindered by limited data at this early stage of adoption. While different model calibrations can replicate current trends, they often yield divergent forecasts. Using empirical data from states with the highest levels of ado",
    "data_url": "",
    "source": "CrossRef",
    "direction": "FLEXERGY",
    "subcategory": "Electric Vehicles & Mobility",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Deep learning predicts real-world electric vehicle direct current charging profiles and durations",
    "doi": "10.1038/s41467-025-65970-y",
    "url": "https://doi.org/10.1038/s41467-025-65970-y",
    "journal": "Nature Communications",
    "year": 2025,
    "authors": "Li, S.; Zhang, M.; Doel, R.; Ross, B.; Piggott, M.",
    "abstract": "Abstract\n                  Accurate prediction of electric vehicle charging profiles and durations is critical for adoption and optimising infrastructure. Direct current fast charging presents complex behaviours shaped by many factors. This work introduces a deep learning framework trained on 909,135 real-world sessions, capable of predicting charging profiles and durations from minimal input with uncertainty estimates. The model initiates predictions from a single point on the power and state-o",
    "data_url": "",
    "source": "CrossRef",
    "direction": "FLEXERGY",
    "subcategory": "Electric Vehicles & Mobility",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Atmospheric wind energization of ocean weather",
    "doi": "10.1038/s41467-025-56310-1",
    "url": "https://doi.org/10.1038/s41467-025-56310-1",
    "journal": "Nature Communications",
    "year": 2025,
    "authors": "Rai, S.; Farrar, J.; Aluie, H.",
    "abstract": "Abstract\n                  \n                    Ocean weather comprises vortical and straining mesoscale motions, which play fundamentally different roles in the ocean circulation and climate system. Vorticity determines the movement of major ocean currents and gyres. Strain contributes to frontogenesis and the deformation of water masses, driving much of the mixing and vertical transport in the upper ocean. While recent studies have shown that interactions with the atmosphere damp the ocean’s m",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "A machine learning model for hub-height short-term wind speed prediction",
    "doi": "10.1038/s41467-025-58456-4",
    "url": "https://doi.org/10.1038/s41467-025-58456-4",
    "journal": "Nature Communications",
    "year": 2025,
    "authors": "Zhang, Z.; Lin, L.; Gao, S.; Wang, J.; Zhao, H.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Trends in vertical wind velocity variability reveal cloud microphysical feedback",
    "doi": "10.1038/s41467-025-67541-7",
    "url": "https://doi.org/10.1038/s41467-025-67541-7",
    "journal": "Nature Communications",
    "year": 2025,
    "authors": "Barahona, D.; Breen, K.; Ngo, D.; Maciel, F.; Patnaude, R.",
    "abstract": "Abstract\n                  \n                    By controlling supersaturation vertical air motion influences how aerosols activate into cloud droplets and ice crystals. This effect is difficult to represent accurately in atmospheric models as they cannot typically resolve the sub-kilometer scale component of wind motion, however it can be addressed by machine learning. Here we apply a generative technique combining storm-resolving simulations, observational and climate reanalysis data, to predi",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Weather & Meteorological Data",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Bioinspired nondissipative mechanical energy storage and release in hydrogels via hierarchical sequentially swollen stretched chains",
    "doi": "10.1038/s41467-025-59743-w",
    "url": "https://doi.org/10.1038/s41467-025-59743-w",
    "journal": "Nature Communications",
    "year": 2025,
    "authors": "Savolainen, H.; Hosseiniyan, N.; Piedrahita-Bello, M.; Ikkala, O.",
    "abstract": "Abstract\n                  Nature suggests concepts for materials with efficient mechanical energy storage and release, i.e., resilience, involving small energy dissipation upon mechanical loading and unloading, such as in resilin and elastin. These materials facilitate burst-like movements involving high stiffness and low strain and high reversibility. Synthetic hydrogels that allow highly reversible mechanical energy storage have remained a challenge, despite mimicking biological soft tissues.",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Machine learning assisted composition design of high-entropy Pb-free relaxors with giant energy-storage",
    "doi": "10.1038/s41467-025-56443-3",
    "url": "https://doi.org/10.1038/s41467-025-56443-3",
    "journal": "Nature Communications",
    "year": 2025,
    "authors": "Wang, X.; Zhang, J.; Ma, X.; Luo, H.; Liu, L.",
    "abstract": "Abstract\n          The high-entropy strategy has emerged as a prevalent approach to boost capacitive energy-storage performance of relaxors for advanced electrical and electronic systems. However, exploring high-performance high-entropy systems poses challenges due to the extensive compositional space. Herein, with the assistance of machine learning screening, we demonstrated a high energy-storage density of 20.7 J cm-3 with a high efficiency of 86% in a high-entropy Pb-free relaxor ceramic. A r",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "AI & Deep Learning"
  },
  {
    "title": "Unlocking global carbon reduction potential by embracing low-carbon lifestyles",
    "doi": "10.1038/s41467-025-59269-1",
    "url": "https://doi.org/10.1038/s41467-025-59269-1",
    "journal": "Nature Communications",
    "year": 2025,
    "authors": "Guan, Y.; Shan, Y.; Hang, Y.; Nie, Q.; Liu, Y.",
    "abstract": "Abstract\n          Low-carbon lifestyles provide demand-side solutions to meet global climate targets, yet the global carbon-saving potential of consumer-led abatement actions remains insufficiently researched. Here, we quantify the greenhouse gas emissions reduction potential of 21 low-carbon expenditures using a global multi-regional input-output model linked with detailed household expenditure data. Targeting households exceeding the global per-capita average required to stay below 2 degrees,",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "Carbon Trading & New Business Models",
    "refined_category": "Demand Response & IoT"
  },
  {
    "title": "Dynamic grid management reduces wildfire adaptation costs in the electric power sector",
    "doi": "10.1038/s41558-025-02436-5",
    "url": "https://doi.org/10.1038/s41558-025-02436-5",
    "journal": "Nature Climate Change",
    "year": 2025,
    "authors": "Warner, C.; Callaway, D.; Fowlie, M.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Cost-effective adaptation of electric grids",
    "doi": "10.1038/s41558-025-02421-y",
    "url": "https://doi.org/10.1038/s41558-025-02421-y",
    "journal": "Nature Climate Change",
    "year": 2025,
    "authors": "Wang, Z.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Single-fibril Förster resonance energy transfer imaging and deep learning reveal concentration dependence of amyloid β 42 aggregation pathways",
    "doi": "10.1093/pnasnexus/pgaf342",
    "url": "https://doi.org/10.1093/pnasnexus/pgaf342",
    "journal": "npj Clean Energy",
    "year": 2025,
    "authors": "Sohail, S.; Yoo, J.; Chung, H.",
    "abstract": "Abstract\n                  Amyloid fibril formation is a highly heterogeneous process as evidenced by polymorphism in fibril structure. It has been suggested that different polymorphs are associated with different diseases or disease subtypes. Detailed characterization of this heterogeneity is a key to understanding the aggregation mechanism and, possibly, the disease mechanism. In this work, we develop Förster resonance energy transfer (FRET) imaging of amyloid fibril formation in real time and",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "AI & Deep Learning"
  },
  {
    "title": "Spatiotemporal predictions of toxic urban plumes using deep learning",
    "doi": "10.1093/pnasnexus/pgaf198",
    "url": "https://doi.org/10.1093/pnasnexus/pgaf198",
    "journal": "npj Clean Energy",
    "year": 2025,
    "authors": "Wang, Y.; Fernández-Godino, M.; Gunawardena, N.; Lucas, D.; Yue, X.",
    "abstract": "Abstract\n               Industrial accidents, chemical spills, and structural fires can release large amounts of harmful materials that disperse into urban atmospheres and impact populated areas. Computer models are typically used to predict the transport of toxic plumes by solving fluid dynamical equations. However, these models can be computationally expensive due to the need for many grid cells to simulate turbulent flow and resolve individual buildings and streets. In emergency response situ",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Versatile phenolic composites by in situ polymerization of concentrated dispersions of carbon nanotubes",
    "doi": "10.1093/pnasnexus/pgaf274",
    "url": "https://doi.org/10.1093/pnasnexus/pgaf274",
    "journal": "npj Clean Energy",
    "year": 2025,
    "authors": "Yu, Z.; Zhang, C.; Chen, M.; Huang, J.",
    "abstract": "Abstract\n               Uniform dispersion of carbon nanotubes in a polymer matrix is a prerequisite for high-performance nanotube-based composites. Here, we report an in situ polymerization route to synthesize a range of phenolic composites with high loading of single-wall carbon nanotubes (SWCNTs, >40 wt%) and continuously tunable viscoelasticity. SWCNTs can be directly and uniformly dispersed in cresols through noncovalent charge-transfer interactions without the need for surfactants, ",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Air-conditioning replacement to enhance the reliability of renewable power systems under extreme weather risks",
    "doi": "10.1093/pnasnexus/pgaf230",
    "url": "https://doi.org/10.1093/pnasnexus/pgaf230",
    "journal": "npj Clean Energy",
    "year": 2025,
    "authors": "Zhu, L.; Liang, Z.; Yan, Z.; Ming, X.; Duan, H.",
    "abstract": "Abstract\n               The increasing demand for residential heating and cooling significantly affects power systems, especially during extreme weather events. The replacement of outdated room air-conditioning (RAC) with a high-efficiency model demonstrated considerable potential in alleviating this effect. In this study, the impacts of extreme warm, cold, and drought events on power demand and supply are explored. By simulating residential heating and cooling loads in southern Chinese cities a",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "The equity implications of pecuniary externalities on an electric grid",
    "doi": "10.1093/pnasnexus/pgaf356",
    "url": "https://doi.org/10.1093/pnasnexus/pgaf356",
    "journal": "npj Clean Energy",
    "year": 2025,
    "authors": "Sims, C.; Ali, G.; Holladay, J.; Roberson, T.; Chen, C.",
    "abstract": "Abstract\n                  The adoption of rooftop photovoltaic (PV) systems can create upward pressure on retail electricity rates as utilities are forced to spread their fixed costs of generation and transmission across a smaller customer base. Since high-income households are more likely to purchase PV systems, low-income households may be disproportionately impacted by these rate increases. Using a novel combination of agent-based computational economic modeling and a choice experiment of ro",
    "data_url": "",
    "source": "CrossRef",
    "direction": "CleanTech",
    "subcategory": "Solar PV & Storage",
    "direction_label": "Novel Low/Zero Carbon Technologies",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Multicriteria models provide enhanced insight for siting US offshore wind",
    "doi": "10.1093/pnasnexus/pgaf051",
    "url": "https://doi.org/10.1093/pnasnexus/pgaf051",
    "journal": "npj Clean Energy",
    "year": 2025,
    "authors": "Santarromana, R.; Abdulla, A.; Morgan, M.; Mendonça, J.",
    "abstract": "Abstract\n               Offshore wind can be a key contributor to energy system decarbonization, but its deployment in certain regions has been slow, partly due to opposition from disparate interests. Failure to sufficiently address the concerns of external stakeholders could continue to hamper deployment. Here, we use a multi criteria model to assess all possible sites in a 2 km × 2 km grid of all potential locations in continental US federal waters, contrasting the perspectives of developers a",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Renewable Energy Resource Mapping",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Grid connections and inequitable access to electricity in African cities",
    "doi": "10.1038/s44284-025-00221-1",
    "url": "https://doi.org/10.1038/s44284-025-00221-1",
    "journal": "Nature Cities",
    "year": 2025,
    "authors": "Kersey, J.; Massa, C.; Lukuyu, J.; Mbabazi, J.; Taneja, J.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Control strategy evaluation for reactive power management in grid-connected photovoltaic systems under varying solar conditions",
    "doi": "10.1038/s41598-025-08918-y",
    "url": "https://doi.org/10.1038/s41598-025-08918-y",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Adak, S.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "CleanTech",
    "subcategory": "Solar PV & Storage",
    "direction_label": "Novel Low/Zero Carbon Technologies",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Photovoltaic solar energy prediction using the seasonal-trend decomposition layer and ASOA optimized LSTM neural network model",
    "doi": "10.1038/s41598-025-87625-0",
    "url": "https://doi.org/10.1038/s41598-025-87625-0",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Mohanasundaram, V.; Rangaswamy, B.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "CleanTech",
    "subcategory": "Solar PV & Storage",
    "direction_label": "Novel Low/Zero Carbon Technologies",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Optimizing photovoltaic power plant forecasting with dynamic neural network structure refinement",
    "doi": "10.1038/s41598-024-80424-z",
    "url": "https://doi.org/10.1038/s41598-024-80424-z",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Díaz-Bello, D.; Vargas-Salgado, C.; Alcazar-Ortega, M.; Alfonso-Solar, D.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "CleanTech",
    "subcategory": "Solar PV & Storage",
    "direction_label": "Novel Low/Zero Carbon Technologies",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Seasonal quantile forecasting of solar photovoltaic power using Q-CNN-GRU",
    "doi": "10.1038/s41598-025-12797-8",
    "url": "https://doi.org/10.1038/s41598-025-12797-8",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Ait Mouloud, L.; Kheldoun, A.; Oussidhoum, S.; Alharbi, H.; Alotaibi, S.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "CleanTech",
    "subcategory": "Solar PV & Storage",
    "direction_label": "Novel Low/Zero Carbon Technologies",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "An improved weighted average algorithm with Cloud-Based Risk-Conscious stochastic model for building energy optimization",
    "doi": "10.1038/s41598-025-30043-z",
    "url": "https://doi.org/10.1038/s41598-025-30043-z",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Keawsawasvong, S.; Jearsiripongkul, T.; Khajehzadeh, M.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Building retrofit multiobjective optimization using neural networks and genetic algorithm three for energy carbon and comfort",
    "doi": "10.1038/s41598-025-21871-0",
    "url": "https://doi.org/10.1038/s41598-025-21871-0",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Duan, Z.; Li, B.; Zi, Y.; Yao, G.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Smart building energy management with renewables and storage systems using a modified weighted mean of vectors algorithm",
    "doi": "10.1038/s41598-024-79782-5",
    "url": "https://doi.org/10.1038/s41598-024-79782-5",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Ebeed, M.; hassan, S.; Kamel, S.; Nasrat, L.; Mohamed, A.",
    "abstract": "Abstract\n          With the advancement of automation technologies in household appliances, the flexibility of smart home energy management (EM) systems has increased. However, this progress has brought about a new challenge for smart homes: the EM has become more complex with the integration of multiple conventional, renewable, and energy storage systems. To address this challenge, a novel modified Weighted Mean of Vectors algorithm (MINFO) is proposed. This algorithm aims to enhance the perfor",
    "data_url": "",
    "source": "CrossRef",
    "direction": "FLEXERGY",
    "subcategory": "Smart Home & EMS",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "Demand Response & IoT"
  },
  {
    "title": "Deep reinforcement learning based low energy consumption scheduling approach design for urban electric logistics vehicle networks",
    "doi": "10.1038/s41598-025-92916-7",
    "url": "https://doi.org/10.1038/s41598-025-92916-7",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Sun, P.; He, J.; Wan, J.; Guan, Y.; Liu, D.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Spatiotemporal evolution of agricultural carbon emissions intensity in China and analysis of influencing factors",
    "doi": "10.1038/s41598-025-04973-7",
    "url": "https://doi.org/10.1038/s41598-025-04973-7",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Zhu, X.; Shao, X.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Demand Response & IoT"
  },
  {
    "title": "Individual perceptions of renewable energy investment in Somali firms",
    "doi": "10.1038/s41598-025-11581-y",
    "url": "https://doi.org/10.1038/s41598-025-11581-y",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Nor, B.",
    "abstract": "Abstract\n          Somalia’s energy sector is seen as potential for development and investment. financing this sector is crucial for development and economic growth. Small and medium-sized private-sector enterprises are the primary electricity generators and distributors, operating diesel-powered systems via off-grid networks This study investigates the factors influencing investment intentions in renewable energy in Somalia. This study utilized a quantitative research approach employing a descr",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Optimal energy management of multi-carrier energy system considering uncertainty in renewable generation",
    "doi": "10.1038/s41598-025-10404-4",
    "url": "https://doi.org/10.1038/s41598-025-10404-4",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Garg, A.; Niazi, K.; Tiwari, S.; Sharma, S.; Rawat, T.",
    "abstract": "Abstract\n          This paper presents a structured approach for the efficient operation of multi-carrier energy systems under the uncertainty of renewable energy sources. As the penetration of wind and solar energy increases, managing the resulting variability becomes critical to maintaining both economic efficiency and operational flexibility. To address this, a two-stage multi objective optimization framework is proposed. In the first stage, the objective is to minimize daily operational cost",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "Novel Low/Zero Carbon Technologies",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Optimal energy management for multi-energy microgrids using hybrid solutions to address renewable energy source uncertainty",
    "doi": "10.1038/s41598-025-90062-8",
    "url": "https://doi.org/10.1038/s41598-025-90062-8",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Ramkumar, M.; Subramani, J.; Sivaramkrishnan, M.; Munimathan, A.; Michael, G.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "Carbon Trading & New Business Models",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Capabilities of battery and compressed air storage in the economic energy scheduling and flexibility regulation of multi-microgrids including non-renewable/renewable units",
    "doi": "10.1038/s41598-025-06768-2",
    "url": "https://doi.org/10.1038/s41598-025-06768-2",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Naghibi, A.; Akbari, E.; Veisi, M.; Shahmoradi, S.; Pirouzi, S.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "Carbon Trading & New Business Models",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Power quality disturbance identification using hybrid deep learning in renewable energy systems",
    "doi": "10.1038/s41598-025-28291-0",
    "url": "https://doi.org/10.1038/s41598-025-28291-0",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Peruman, P.; Ayyar, K.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "AI & Deep Learning"
  },
  {
    "title": "Multi-criteria assessment of optimization methods for controlling renewable energy sources in distribution systems",
    "doi": "10.1038/s41598-025-20339-5",
    "url": "https://doi.org/10.1038/s41598-025-20339-5",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Eid, A.; Alsafrani, A.",
    "abstract": "Abstract\n          Numerous optimization techniques have recently been employed in the literature to enhance various electric power systems. Optimization algorithms help system operators determine the optimal location and capacity of any renewable energy source (RES) connected to a system, enabling them to achieve a specific goal and improve its performance. This study presents a novel statistical evaluation of 20 famous metaheuristic optimization techniques based on 10 performance measures. The",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "A bi-level optimization strategy of electricity-hydrogen-carbon integrated energy system considering photovoltaic and wind power uncertainty and demand response",
    "doi": "10.1038/s41598-024-84605-8",
    "url": "https://doi.org/10.1038/s41598-024-84605-8",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Lu, M.; Teng, Y.; Chen, Z.; Song, Y.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "CleanTech",
    "subcategory": "Solar PV & Storage",
    "direction_label": "Novel Low/Zero Carbon Technologies",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Federated two-edge graph attention network with weighted global aggregation for electricity consumption demand forecasting",
    "doi": "10.1038/s41598-025-28610-5",
    "url": "https://doi.org/10.1038/s41598-025-28610-5",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Yang, M.; Ren, J.; Zeng, L.; Yang, X.; Li, S.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Multi-temporal dimension prediction of new energy electricity demand based on chaos-LSSVM neural network",
    "doi": "10.1038/s41598-025-27677-4",
    "url": "https://doi.org/10.1038/s41598-025-27677-4",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Wu, Y.; Wang, W.; Ma, X.; Zhao, R.; Wu, B.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Seasonal forecasting of the hourly electricity demand applying machine and deep learning algorithms impact analysis of different factors",
    "doi": "10.1038/s41598-025-91878-0",
    "url": "https://doi.org/10.1038/s41598-025-91878-0",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "El-Azab, H.; Swief, R.; El-Amary, N.; Temraz, H.",
    "abstract": "Abstract\n          The purpose of this paper is to suggest short-term Seasonal forecasting for hourly electricity demand in the New England Control Area (ISO-NE-CA). Precision improvements are also considered when creating a model. Where the whole database is split into four seasons based on demand patterns. This article’s integrated model is built on techniques for machine and deep learning methods: Adaptive Neural-based Fuzzy Inference System, Long Short-Term Memory, Gated Recurrent Units, and",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "An optimization method for integrated demand response strategies for electricity and heat considering the uncertainty of user-side loads",
    "doi": "10.1038/s41598-025-30090-6",
    "url": "https://doi.org/10.1038/s41598-025-30090-6",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Li, J.; Zhang, D.; Wei, Y.; Zhou, X.; Kong, X.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "FLEXERGY",
    "subcategory": "Demand Response",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Robust fuzzy dynamic integrated environmental-economic-social scheduling considering demand response and user’s satisfaction with electricity under multiple uncertainties",
    "doi": "10.1038/s41598-025-87689-y",
    "url": "https://doi.org/10.1038/s41598-025-87689-y",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Zhang, H.; Xi, Q.; Chen, L.; Min, Y.; Fan, X.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "FLEXERGY",
    "subcategory": "Demand Response",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "The electricity purchasing and selling strategy of load aggregators participating in China’s dual-tier electricity market considering inter-provincial subsidies",
    "doi": "10.1038/s41598-025-13385-6",
    "url": "https://doi.org/10.1038/s41598-025-13385-6",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Zhang, H.; Tian, Y.; Liu, X.; Kuang, M.; Zhang, N.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "Carbon Trading & New Business Models",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Systematic hyperparameter analysis of GRU and LSTM across demand pattern types: a demand-characteristic-driven meta-learning framework for rapid optimization",
    "doi": "10.1038/s41598-025-31508-x",
    "url": "https://doi.org/10.1038/s41598-025-31508-x",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "El-Meehy, A.; El-Kharbotly, A.; El-Beheiry, M.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Time series transformer for tourism demand forecasting",
    "doi": "10.1038/s41598-025-15286-0",
    "url": "https://doi.org/10.1038/s41598-025-15286-0",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Yi, S.; Chen, X.; Tang, C.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Demand forecasting of smart tourism integrating spatial metrology and deep learning",
    "doi": "10.1038/s41598-025-26830-3",
    "url": "https://doi.org/10.1038/s41598-025-26830-3",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Ma, J.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Climate-adaptive energy forecasting in green buildings via attention-enhanced Seq2Seq transfer learning",
    "doi": "10.1038/s41598-025-16953-y",
    "url": "https://doi.org/10.1038/s41598-025-16953-y",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Peng, F.; Su, T.; Zeng, Q.; Han, X.",
    "abstract": "Abstract\n          Energy consumption forecasting in green buildings remains challenging due to complex climate-building interactions and temporal dependencies in energy usage patterns. Existing prediction models often fail to capture long-term dependencies and adapt to diverse climatic conditions, limiting their practical applicability. This study presents an integrated forecasting framework that combines sequence-to-sequence (Seq2Seq) architecture with reinforcement learning and transfer learn",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Solar potential assessment using machine learning and climate change projections for long-term energy planning",
    "doi": "10.1038/s41598-025-23661-0",
    "url": "https://doi.org/10.1038/s41598-025-23661-0",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Reddy, B.; Gautam, K.; Pachauri, N.",
    "abstract": "Abstract\n                  This work proposes a novel method for evaluating solar potential, essential for the development, installation, and operation of solar power systems. The approach forecasts solar energy potential for specific sites by utilizing integrated geospatial, meteorological, and infrastructural multidimensional data. A new application has been released to assess the solar capacity globally. The study evaluated various machine learning methods, ultimately selecting an XGBoost mod",
    "data_url": "",
    "source": "CrossRef",
    "direction": "CleanTech",
    "subcategory": "Solar PV & Storage",
    "direction_label": "Novel Low/Zero Carbon Technologies",
    "refined_category": "AI & Deep Learning"
  },
  {
    "title": "Anomaly detection with grid sentinel framework for electric vehicle charging stations in a smart grid environment",
    "doi": "10.1038/s41598-025-00400-z",
    "url": "https://doi.org/10.1038/s41598-025-00400-z",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Kesavan, V.; Hossen, M.; Gopi, R.; Joseph, E.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "FLEXERGY",
    "subcategory": "Electric Vehicles & Mobility",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "An online learning method for assessing smart grid stability under dynamic perturbations",
    "doi": "10.1038/s41598-025-94718-3",
    "url": "https://doi.org/10.1038/s41598-025-94718-3",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Alaerjan, A.; Jabeur, R.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Design of a hybrid learning model for establishing consistency in smart grid environment",
    "doi": "10.1038/s41598-025-28986-4",
    "url": "https://doi.org/10.1038/s41598-025-28986-4",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Mahendran, S.; Gomathy, B.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Improving efficiency in smart grid monitoring using hybrid classification and dimensionality reduction",
    "doi": "10.1038/s41598-025-26009-w",
    "url": "https://doi.org/10.1038/s41598-025-26009-w",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Kumar, T.; Kesavaraja, D.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Robust algorithm development of frequency estimation in smart grid",
    "doi": "10.1038/s41598-025-16533-0",
    "url": "https://doi.org/10.1038/s41598-025-16533-0",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Yu, Y.; Yang, Y.; Wang, X.; Lv, L.; Chen, Y.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "A smart grid data sharing scheme supporting policy update and traceability",
    "doi": "10.1038/s41598-025-10704-9",
    "url": "https://doi.org/10.1038/s41598-025-10704-9",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Yang, X.; Yao, K.; Li, S.; Du, X.; Wang, C.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Optimal micro-grid battery scheduling within a comprehensive smart pricing scheme",
    "doi": "10.1038/s41598-025-02690-9",
    "url": "https://doi.org/10.1038/s41598-025-02690-9",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Ali, M.; Besheer, A.; Emara, H.; Bahgat, A.",
    "abstract": "Abstract\n          The challenge of optimizing battery operating revenue while mitigating aging costs remains inadequately addressed in current literature. This paper introduces a novel cost–benefit approach for scheduling battery energy storage systems (BESS) within microgrids (MGs) that features smart grid attributes. The proposed comprehensive approach accounts for fluctuations of real-time pricing, demand charge tariffs, and battery degradation cost. Using the dynamic programming technique, ",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "Carbon Trading & New Business Models",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Artificial intelligence-augmented smart grid architecture for cyber intrusion detection and mitigation in electric vehicle charging infrastructure",
    "doi": "10.1038/s41598-025-04984-4",
    "url": "https://doi.org/10.1038/s41598-025-04984-4",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Sharma, A.; Rani, S.; Shabaz, M.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "FLEXERGY",
    "subcategory": "Electric Vehicles & Mobility",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Interpretable adaptive fault detection method for smart grid based on belief rule base",
    "doi": "10.1038/s41598-025-91897-x",
    "url": "https://doi.org/10.1038/s41598-025-91897-x",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Li, Y.; Bai, Y.; Yang, R.; Feng, Z.; He, W.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Identification and suppression of low-frequency oscillations using PMU measurements based power system model in smart grid",
    "doi": "10.1038/s41598-025-88389-3",
    "url": "https://doi.org/10.1038/s41598-025-88389-3",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Zuhaib, M.; Rihan, M.; Gupta, S.; Sufyan, M.",
    "abstract": "Abstract\n          Low-frequency oscillations (LFO) are inherent to large interconnected power systems. Timely detection and mitigation of these oscillations is essential to maintain reliable power system operation. This paper presents a methodology to identify and mitigate low-frequency oscillations ( forced and inter-area) using a wide area monitoring system (WAMS) based power system model utilizing phasor measurement units (PMUs). These models accurately identify the behavior and location of ",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Renewable Energy Simulation Tools",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Effect of thermal stress on the life of DC link capacitors for smart grid",
    "doi": "10.1038/s41598-025-88522-2",
    "url": "https://doi.org/10.1038/s41598-025-88522-2",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Sun, X.; Qiao, Y.; Li, Y.; Cao, C.; Guo, X.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Flexible renewable integrated energy system capabilities to improve voltage stability with power quality and economic environmental operation of smart grid",
    "doi": "10.1038/s41598-025-29052-9",
    "url": "https://doi.org/10.1038/s41598-025-29052-9",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Hassankashi, A.; Dini, A.; Pirouzi, S.; Veisi, M.; Bahreini, M.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Novel machine learning approach for enhanced smart grid power use and price prediction using advanced shark Smell-Tuned flexible support vector machine",
    "doi": "10.1038/s41598-025-05083-0",
    "url": "https://doi.org/10.1038/s41598-025-05083-0",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Duan, Y.; Xu, Z.; Chen, H.; Wang, Y.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "AI-driven smart grid optimization for hospital energy systems integrating renewable generation, predictive maintenance, and resilient infrastructure",
    "doi": "10.1038/s41598-025-28907-5",
    "url": "https://doi.org/10.1038/s41598-025-28907-5",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Sarker, M.; Ramasamy, G.; Al Qwaid, M.; Hossen, M.; Sadeque, M.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Enhanced FPGA-based smart power grid simulation using Heun and Piecewise analytic method",
    "doi": "10.1038/s41598-025-18105-8",
    "url": "https://doi.org/10.1038/s41598-025-18105-8",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Gul, U.; Raza Ur Rehman, H.; Gul, M.; Mezquita, G.; Barrera, A.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Prediction of electricity consumption and hydropower production in the smart power grid based on the gated recurrent unit neural network and modified future search algorithm",
    "doi": "10.1038/s41598-025-32294-2",
    "url": "https://doi.org/10.1038/s41598-025-32294-2",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Tang, H.; Wang, Y.; Yuan, X.; Razmjooy, N.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Resilient cybersecurity in smart grid ICS communication using BLAKE3-driven dynamic key rotation and intrusion detection",
    "doi": "10.1038/s41598-025-17530-z",
    "url": "https://doi.org/10.1038/s41598-025-17530-z",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Dokku, N.; David Amar Raj, R.; Bodapati, S.; Pallakonda, A.; Reddy, Y.",
    "abstract": "Abstract\n          The increasing convergence of Industrial Control Systems (ICS) with critical infrastructure, such as smart grids, has increased their exposure to advanced cyber threats, demanding advanced security frameworks to maintain security and operational integrity. This paper shows an innovative cybersecurity approach for ICS, using the IEC 60870-5-104 dataset, that combines machine learning, cryptographic resilience, and forensic analysis to predict and neutralize various attack vecto",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Grid tied hybrid PV fuel cell system with energy storage and ANFIS based MPPT for smart EV charging",
    "doi": "10.1038/s41598-025-09626-3",
    "url": "https://doi.org/10.1038/s41598-025-09626-3",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "vendoti, S.; Tulasi, N.; Jalli, R.; Ponnuru, S.; Jin, Z.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "FLEXERGY",
    "subcategory": "Electric Vehicles & Mobility",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Multi objective moth swarm algorithm for optimizing electric vehicle integration in distribution grids",
    "doi": "10.1038/s41598-025-10849-7",
    "url": "https://doi.org/10.1038/s41598-025-10849-7",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Azadikhouy, M.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "FLEXERGY",
    "subcategory": "Electric Vehicles & Mobility",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Multi-objective route optimization for electric vehicle hazardous materials transportation in uncertain environments",
    "doi": "10.1038/s41598-025-32134-3",
    "url": "https://doi.org/10.1038/s41598-025-32134-3",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Zhang, Q.; Zhang, Z.; Ma, C.",
    "abstract": "Abstract\n                  This paper focuses on the application of electric vehicles in the transportation of Category 9 hazardous materials. Given the high requirements for safety and timeliness in hazardous materials transportation, this study first comprehensively considers the impacts of population density uncertainty and cargo volume changes on transportation risks and power consumption. Furthermore, a multi-objective path optimization model is developed. The model aims to minimize transpo",
    "data_url": "",
    "source": "CrossRef",
    "direction": "FLEXERGY",
    "subcategory": "Electric Vehicles & Mobility",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Carbon management in massive electric vehicle temporal and spatial scheduling with automotive electronic forensics",
    "doi": "10.1038/s41598-025-93798-5",
    "url": "https://doi.org/10.1038/s41598-025-93798-5",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Cao, Y.; Zhang, Y.; Zhao, C.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "FLEXERGY",
    "subcategory": "Electric Vehicles & Mobility",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Survival analysis of electric vehicle charging behavior and the temporal evolution of feature effects",
    "doi": "10.1038/s41598-025-18771-8",
    "url": "https://doi.org/10.1038/s41598-025-18771-8",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Meža, M.; Strle, G.; Meža, M.",
    "abstract": "Abstract\n          This study proposes a survival-based modeling framework that combines behavioral features with interpretable machine learning to understand and predict user churn in electric vehicle charging services. Using a dataset of 1,074 users and 107,531 charging sessions from Central European countries, we modeled time-to-churn while handling censored observations. The best-performing model, a Stacked Weibull survival model based on gradient boosting, achieved a concordance index of 0.",
    "data_url": "",
    "source": "CrossRef",
    "direction": "FLEXERGY",
    "subcategory": "Electric Vehicles & Mobility",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "AI & Deep Learning"
  },
  {
    "title": "Machine learning-based approach for reduction of energy consumption in hybrid energy storage electric vehicle",
    "doi": "10.1038/s41598-025-11330-1",
    "url": "https://doi.org/10.1038/s41598-025-11330-1",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Paulraj, T.; Obulesu, Y.",
    "abstract": "Abstract\n          This research introduces a novel machine learning-based strategy for generating supercapacitor (SC) reference current to optimize energy distribution in Battery Electric Vehicles (BEV) and Hybrid Battery Electric Vehicles (HBEV). A Long Short-Term Memory (LSTM) neural network is trained using real-world drive cycle data and exported in Open Neural Network Exchange (ONNX) format for real-time deployment within a Simulink-based control environment. This enables adaptive SC curre",
    "data_url": "",
    "source": "CrossRef",
    "direction": "FLEXERGY",
    "subcategory": "Electric Vehicles & Mobility",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Location allocation and capacity optimization for a PV and battery integrated hybrid community electric vehicle charging station",
    "doi": "10.1038/s41598-025-31865-7",
    "url": "https://doi.org/10.1038/s41598-025-31865-7",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Kayal, P.; Braciník, P.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "FLEXERGY",
    "subcategory": "Electric Vehicles & Mobility",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Smart Strategies for Improving Electric Vehicle Battery Performance and Efficiency",
    "doi": "10.1038/s41598-025-25987-1",
    "url": "https://doi.org/10.1038/s41598-025-25987-1",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Tangi, S.; Vatsa, A.; Opam, A.; Bonthagorla, P.; Gaonkar, D.",
    "abstract": "Abstract\n                  The increasing demand for Electric Vehicles (EVs) necessitates accurate range prediction and optimization of driving parameters to address range anxiety and improve user experience. This study proposes a machine learning-based framework for predicting EV range, optimum acceleration, and velocity using a synthetically generated dataset of 2,000 samples designed to reflect real-world driving scenarios. Four models—Random Forest (RF), Extra Trees (ET), Linear Regression (",
    "data_url": "",
    "source": "CrossRef",
    "direction": "FLEXERGY",
    "subcategory": "Electric Vehicles & Mobility",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "New flexible bidirectional converter for electric vehicle substations connecting microgrids",
    "doi": "10.1038/s41598-025-19277-z",
    "url": "https://doi.org/10.1038/s41598-025-19277-z",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Vinh, N.; Nguyen, V.; Van Dung, N.; Vu, H.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "FLEXERGY",
    "subcategory": "Electric Vehicles & Mobility",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Strategic design of wind energy and battery storage for efficient and sustainable energy systems",
    "doi": "10.1038/s41598-025-18863-5",
    "url": "https://doi.org/10.1038/s41598-025-18863-5",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Eroğlu, H.; Kurtuluş, O.",
    "abstract": "Abstract\n          The intermittent nature of renewable energy sources, particularly wind power, necessitates advanced energy management and storage strategies to ensure grid stability and economic viability. This study investigates the techno economic benefits of integrating Battery Energy Storage Systems (BESS) into wind power plants by developing and evaluating optimized hybrid operation strategies. Using real world Data from a 70 MW wind farm, ten distinct operational strategies were simulat",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Interval-aware optimal control of PMSG-based wind energy conversion systems via piecewise Chebyshev inclusion",
    "doi": "10.1038/s41598-025-26563-3",
    "url": "https://doi.org/10.1038/s41598-025-26563-3",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Razmjooy, N.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Optimizing weak grid integrated wind energy systems using ANFIS-SRF controlled DSTATCOM",
    "doi": "10.1038/s41598-025-98872-6",
    "url": "https://doi.org/10.1038/s41598-025-98872-6",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Ramana, P.; Rosalina, K.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "DDPG algorithm for power optimization and control of solar PV-integrated DFIG wind energy systems",
    "doi": "10.1038/s41598-025-19818-6",
    "url": "https://doi.org/10.1038/s41598-025-19818-6",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Pandey, R.; Bose, S.; Dwivedi, P.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "CleanTech",
    "subcategory": "Solar PV & Storage",
    "direction_label": "Novel Low/Zero Carbon Technologies",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Reducing power ripple for multi-rotor wind energy systems using FOPDPI controllers",
    "doi": "10.1038/s41598-025-96625-z",
    "url": "https://doi.org/10.1038/s41598-025-96625-z",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Benbouhenni, H.; Colak, I.; Elbarbary, Z.; Irshad, S.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Predictive riccati control for enhancing power quality using extended pq theory in wind energy-based conversion systems",
    "doi": "10.1038/s41598-025-13782-x",
    "url": "https://doi.org/10.1038/s41598-025-13782-x",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Sundari, K.; Umamaheswari, M.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Design of a distributed power system using solar PV and micro turbine-based wind energy system with a flywheel energy storage",
    "doi": "10.1038/s41598-025-29604-z",
    "url": "https://doi.org/10.1038/s41598-025-29604-z",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Bhavani, T.; Rajababu, D.; Irfan, M.; Rakesh, T.; Sekhar, P.",
    "abstract": "Abstract\n                  As renewable energy sources gain distinction in distributed power generation, micro-grid systems integrating solar photovoltaic (PV), micro-turbine-based wind energy, and flywheel energy storage have developed as sustainable solutions. This paper presents a novel design methodology for a hybrid micro-grid system that optimally integrates these components, ensuring enhanced efficiency, resilience, and stability. In a grid outage or weak-grid scenario, a flywheel provide",
    "data_url": "",
    "source": "CrossRef",
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    "subcategory": "Solar PV & Storage",
    "direction_label": "Novel Low/Zero Carbon Technologies",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Bi-objective operation optimization of regional integrated energy system considering shared energy storage",
    "doi": "10.1038/s41598-025-22502-4",
    "url": "https://doi.org/10.1038/s41598-025-22502-4",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Li, X.; Zhu, L.; Zhao, L.; Zhang, K.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Multi-timescale optimization scheduling of integrated energy systems oriented towards generalized energy storage services",
    "doi": "10.1038/s41598-025-92601-9",
    "url": "https://doi.org/10.1038/s41598-025-92601-9",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Mao, Y.; Cai, Z.; Jiao, X.; Long, D.",
    "abstract": "Abstract\n          This paper addresses the limitations of existing research that focuses on single-sided resources and two-timescale optimization, overlooking the coordinated response of various energy storage resources across different timescales in comprehensive energy systems. To tackle these shortcomings, the study integrates flexible demand-side resources, such as electric vehicles (EVs), hydrogen storage, and air conditioning clusters, as generalized energy storage. It explores their impa",
    "data_url": "",
    "source": "CrossRef",
    "direction": "FLEXERGY",
    "subcategory": "Electric Vehicles & Mobility",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Optimal scheduling of integrated energy system with gas–liquid phase change carbon dioxide energy storage considering multi-layer low-carbon benefits",
    "doi": "10.1038/s41598-025-05438-7",
    "url": "https://doi.org/10.1038/s41598-025-05438-7",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Li, W.; An, G.; Cai, T.; Yang, Q.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
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    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Artificial intelligence powered intelligent energy management framework for hydrogen storage and dispatch in smart microgrids",
    "doi": "10.1038/s41598-025-24408-7",
    "url": "https://doi.org/10.1038/s41598-025-24408-7",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Hassan, M.",
    "abstract": "Abstract\n                  \n                    Hydrogen energy storage is increasingly recognized as a key enabler for enhancing flexibility and reliability in smart microgrids with high shares of renewable energy. However, its practical deployment remains constrained by challenges such as real-time dispatch complexity, forecasting uncertainty, and nonlinear system dynamics. This study presents a novel AI-powered decision-support framework that integrates Long Short-Term Memory (LSTM) neural ne",
    "data_url": "",
    "source": "CrossRef",
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    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "Carbon Trading & New Business Models",
    "refined_category": "Forecasting & Prediction"
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  {
    "title": "Deep neural network-enhanced prediction and carbon footprint analysis of early-age high-performance manufactured sand concrete’s stress–strain behavior",
    "doi": "10.1038/s41598-025-89016-x",
    "url": "https://doi.org/10.1038/s41598-025-89016-x",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Han, L.; Pu, G.; Guo, Q.; Shi, D.; Liu, B.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
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    "direction_label": "Carbon Trading & New Business Models",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Energy consumption prediction of PEVs incorporating traffic flow information",
    "doi": "10.1038/s41598-025-05098-7",
    "url": "https://doi.org/10.1038/s41598-025-05098-7",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Chen, Y.; Song, Z.; Chen, R.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
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    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Optimization of mine ventilation energy consumption based on improved dung beetle algorithm",
    "doi": "10.1038/s41598-025-15263-7",
    "url": "https://doi.org/10.1038/s41598-025-15263-7",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Bingyan, G.; Zhe, K.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
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    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Forecasting Renewable energy and electricity consumption using evolutionary hyperheuristic algorithm",
    "doi": "10.1038/s41598-025-87013-8",
    "url": "https://doi.org/10.1038/s41598-025-87013-8",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Cao, Y.; Yu, J.; Zhong, R.; Munetomo, M.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "TCN-QRNN model for short term energy consumption forecasting with increased accuracy and optimized computational efficiency",
    "doi": "10.1038/s41598-025-14423-z",
    "url": "https://doi.org/10.1038/s41598-025-14423-z",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Mochurad, L.; Levkovych, R.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
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    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Deep learning approach to energy consumption modeling in wastewater pumping systems",
    "doi": "10.1038/s41598-025-23158-w",
    "url": "https://doi.org/10.1038/s41598-025-23158-w",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Piri, J.; Masoudi, B.; Haghighi, M.; Kisi, O.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "AI & Deep Learning"
  },
  {
    "title": "Natural gas bi-level demand response strategies considering incentives and complexities under dynamic pricing",
    "doi": "10.1038/s41598-025-11893-z",
    "url": "https://doi.org/10.1038/s41598-025-11893-z",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Zeng, H.; Zhou, J.; Dai, H.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
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    "subcategory": "Demand Response",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "Demand Response & IoT"
  },
  {
    "title": "An optimized demand response framework for enhancing power system reliability under wind power and EV-induced uncertainty",
    "doi": "10.1038/s41598-025-05482-3",
    "url": "https://doi.org/10.1038/s41598-025-05482-3",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Pakbin, H.; Karimi, A.; Hassanzadeh, M.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
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    "subcategory": "Demand Response",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "Demand Response & IoT"
  },
  {
    "title": "Optimizing microgrid performance a multi-objective strategy for integrated energy management with hybrid sources and demand response",
    "doi": "10.1038/s41598-025-00118-y",
    "url": "https://doi.org/10.1038/s41598-025-00118-y",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Moosavi, M.; Olamaei, J.; Shourkaei, H.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "FLEXERGY",
    "subcategory": "Demand Response",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Multiobjective distribution system operation with demand response to optimize solar hosting capacity, voltage deviation index and network loss",
    "doi": "10.1038/s41598-024-82379-7",
    "url": "https://doi.org/10.1038/s41598-024-82379-7",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Loji, K.; Sharma, S.; Sharma, G.; Rawat, T.",
    "abstract": "AbstractIn this research, demand response impact on the hosting capacity of solar photovoltaic for distribution system is investigated. The suggested solution model is formulated and presented as a tri-objective optimization that consider maximization of solar PV hosting capacity (HC), minimization of network losses (Loss) and maintaining node voltage deviation (VDev) within acceptable limits. These crucial objectives are optimized simultaneously as well as individually. To assess the efficacy o",
    "data_url": "",
    "source": "CrossRef",
    "direction": "CleanTech",
    "subcategory": "Solar PV & Storage",
    "direction_label": "Novel Low/Zero Carbon Technologies",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "An intelligent incentive-based demand response program for exhaustive environment constrained techno-economic analysis of microgrid system",
    "doi": "10.1038/s41598-025-85175-z",
    "url": "https://doi.org/10.1038/s41598-025-85175-z",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Dey, B.; Sharma, G.; Bokoro, P.; Dutta, S.",
    "abstract": "AbstractThe cost-effective scheduling of distributed energy resources through sophisticated optimization algorithms is the main focus of recent work on microgrid energy management. In order to improve load factor and efficiency, load-shifting techniques are frequently used in conjunction with additional complex constraints such as PHEV scheduling and battery life assessment. Pollutant reduction, however, is rarely highlighted as a primary goal. An incentive-based demand response (IBDR) is introd",
    "data_url": "",
    "source": "CrossRef",
    "direction": "FLEXERGY",
    "subcategory": "Demand Response",
    "direction_label": "Carbon Trading & New Business Models",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Optimal scheduling and energy management of a multi-energy microgrid with electric vehicles incorporating decision making approach and demand response",
    "doi": "10.1038/s41598-025-88776-w",
    "url": "https://doi.org/10.1038/s41598-025-88776-w",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Xiao, G.; Liu, H.; Nabatalizadeh, J.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "FLEXERGY",
    "subcategory": "Electric Vehicles & Mobility",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Two-stage multi-objective framework for optimal operation of modern distribution network considering demand response program",
    "doi": "10.1038/s41598-024-83284-9",
    "url": "https://doi.org/10.1038/s41598-024-83284-9",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Elshenawy, M.; Mohamed, A.; Ali, A.; Mosa, M.",
    "abstract": "AbstractTo improve the inadequate reliability of the grid that has led to a worsening energy crisis and environmental issues, comprehensive research on new clean renewable energy and efficient, cost-effective, and eco-friendly energy management technologies is essential. This requires the creation of advanced energy management systems to enhance system reliability and optimize efficiency. Demand-side energy management systems are a superior solution for multiple reasons. Firstly, they empower co",
    "data_url": "",
    "source": "CrossRef",
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    "subcategory": "Demand Response",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Analyzing the impacts of employing demand response and creating optimal coalition on optimal scheduling of multi-microgrid",
    "doi": "10.1038/s41598-025-95863-5",
    "url": "https://doi.org/10.1038/s41598-025-95863-5",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Altimania, M.; Rostami , R.; Hosseinnia, H.; Alromithy, F.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
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    "subcategory": "Demand Response",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Day-ahead economic dispatch of wind-integrated microgrids using coordinated energy storage and hybrid demand response strategies",
    "doi": "10.1038/s41598-025-11561-2",
    "url": "https://doi.org/10.1038/s41598-025-11561-2",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Meng, Q.; He, Y.; Hussain, S.; Lu, J.; Guerrero, J.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
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    "subcategory": "Demand Response",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Multi-objective optimization of gamified demand response for PV-integrated microgrids: a novel NSGA-III framework with behavioral adaptation modeling",
    "doi": "10.1038/s41598-025-13904-5",
    "url": "https://doi.org/10.1038/s41598-025-13904-5",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Duan, Y.; Gao, C.; Zhang, J.; Wu, Y.; Zhou, S.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "FLEXERGY",
    "subcategory": "Demand Response",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Advanced microgrid optimization using price-elastic demand response and greedy rat swarm optimization for economic and environmental efficiency",
    "doi": "10.1038/s41598-025-86232-3",
    "url": "https://doi.org/10.1038/s41598-025-86232-3",
    "journal": "Scientific Reports",
    "year": 2025,
    "authors": "Singh, A.; Dey, B.; Bajaj, M.; Kadiwala, S.; Kumar, R.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "FLEXERGY",
    "subcategory": "Demand Response",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Grid connection barriers to renewable energy deployment in the United States",
    "doi": "10.1016/j.joule.2024.11.008",
    "url": "https://doi.org/10.1016/j.joule.2024.11.008",
    "journal": "Joule",
    "year": 2025,
    "authors": "Gorman, W.; Kemp, J.; Rand, J.; Seel, J.; Wiser, R.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
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    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Dynamics of disordered intermediates during the two-electron alkaline MnO2 conversion reaction for grid-scale batteries",
    "doi": "10.1016/j.joule.2025.102090",
    "url": "https://doi.org/10.1016/j.joule.2025.102090",
    "journal": "Joule",
    "year": 2025,
    "authors": "Zimmerer, E.; Liang, W.; Somaskandan, R.; DeToma, E.; Fawcett, C.",
    "abstract": "",
    "data_url": "",
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    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "The greenhouse gas burden of weatherizing northern US homes and estimated lifetime energy savings",
    "doi": "10.1016/j.crsus.2025.100550",
    "url": "https://doi.org/10.1016/j.crsus.2025.100550",
    "journal": "Cell Reports Sustainability",
    "year": 2025,
    "authors": "Pfadt-Trilling, A.; Fortier, M.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
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    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Controlling outsourced emissions matters for decarbonizing China’s urban wastewater sector at all life-cycle stages",
    "doi": "10.1016/j.crsus.2025.100323",
    "url": "https://doi.org/10.1016/j.crsus.2025.100323",
    "journal": "Cell Reports Sustainability",
    "year": 2025,
    "authors": "Zhang, L.; Liu, B.; Chen, B.; Chen, S.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Real-time greenhouse gas emission intensity informed demand-side load regulation for power grid decarbonization",
    "doi": "10.1016/j.crsus.2025.100367",
    "url": "https://doi.org/10.1016/j.crsus.2025.100367",
    "journal": "Cell Reports Sustainability",
    "year": 2025,
    "authors": "Li, H.; Shen, G.; Senemmar, S.; Mehmani, A.; Zhang, J.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Spatiotemporal carbon footprints of electricity production and consumption in China",
    "doi": "10.1016/j.crsus.2025.100466",
    "url": "https://doi.org/10.1016/j.crsus.2025.100466",
    "journal": "Cell Reports Sustainability",
    "year": 2025,
    "authors": "Li, Z.; Li, J.; Cao, Z.; Hu, K.; Ao, R.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Demand Response & IoT"
  },
  {
    "title": "Electric vehicle greenhouse gas benefits considering grid emission allocation and charging scheduling",
    "doi": "10.1016/j.crsus.2025.100486",
    "url": "https://doi.org/10.1016/j.crsus.2025.100486",
    "journal": "Cell Reports Sustainability",
    "year": 2025,
    "authors": "Arowosola, A.; Norris, G.; Kirchain, R.; De Kleine, R.; Kim, H.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
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    "subcategory": "Electric Vehicles & Mobility",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Toward understanding the complexity of long-duration energy storage siting in high renewable power grids",
    "doi": "10.1016/j.isci.2025.112571",
    "url": "https://doi.org/10.1016/j.isci.2025.112571",
    "journal": "iScience",
    "year": 2025,
    "authors": "Cole, D.; Dalvi, S.; Zavala, V.; Guerra, O.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Drawing power from a patchwork: Harnessing a decentralized electricity grid",
    "doi": "10.1016/j.isci.2025.113230",
    "url": "https://doi.org/10.1016/j.isci.2025.113230",
    "journal": "iScience",
    "year": 2025,
    "authors": "Heard, B.; Holmes, K.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Assessing the techno-economic benefits of LEMs for different grid topologies and prosumer shares",
    "doi": "10.1016/j.isci.2025.112493",
    "url": "https://doi.org/10.1016/j.isci.2025.112493",
    "journal": "iScience",
    "year": 2025,
    "authors": "Doepfert, M.; Candas, S.; Kraus, H.; Tzscheutschler, P.; Hamacher, T.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Spatiotemporal planning of electric vehicle charging infrastructure: Demand estimation and grid-aware optimization under uncertainty",
    "doi": "10.1016/j.isci.2025.113368",
    "url": "https://doi.org/10.1016/j.isci.2025.113368",
    "journal": "iScience",
    "year": 2025,
    "authors": "Wang, J.; Kaushik, H.; Jacob, R.; Zhang, J.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "FLEXERGY",
    "subcategory": "Electric Vehicles & Mobility",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Earth Grid: Toward a low-carbon energy infrastructure",
    "doi": "10.1016/j.isci.2025.113681",
    "url": "https://doi.org/10.1016/j.isci.2025.113681",
    "journal": "iScience",
    "year": 2025,
    "authors": "Kumar, A.; HE, X.; Deng, Y.; Sah, B.; Singh, A.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Coordinated multi-objective optimization scheduling for electric vehicle swapping station cluster and grid",
    "doi": "10.1016/j.isci.2025.112444",
    "url": "https://doi.org/10.1016/j.isci.2025.112444",
    "journal": "iScience",
    "year": 2025,
    "authors": "Liao, X.; Zheng, Z.; Qian, B.; Wang, H.; Zhan, D.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "FLEXERGY",
    "subcategory": "Electric Vehicles & Mobility",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Adaptive singular spectral decomposition hybrid framework with quadratic error correction for wind power prediction",
    "doi": "10.1016/j.isci.2025.112360",
    "url": "https://doi.org/10.1016/j.isci.2025.112360",
    "journal": "iScience",
    "year": 2025,
    "authors": "Mai, C.; Zhang, L.; Behar, O.; Hu, X.; Chao, X.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Deciphering decarbonization trajectories in China by spatiotemporal-accumulation modeling of electricity carbon footprint",
    "doi": "10.1016/j.isci.2025.111963",
    "url": "https://doi.org/10.1016/j.isci.2025.111963",
    "journal": "iScience",
    "year": 2025,
    "authors": "Tang, J.; Shan, R.; Wang, P.; Chen, W.; Gu, D.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "EnergiTrade",
    "subcategory": "Urban Carbon Footprint",
    "direction_label": "Carbon Trading & New Business Models",
    "refined_category": "Demand Response & IoT"
  },
  {
    "title": "A hybrid demand-side policy for balanced economic emission in microgrid systems",
    "doi": "10.1016/j.isci.2025.112121",
    "url": "https://doi.org/10.1016/j.isci.2025.112121",
    "journal": "iScience",
    "year": 2025,
    "authors": "Singh, A.; Dey, B.; Misra, S.; Kumar, R.; Bajaj, M.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "Carbon Trading & New Business Models",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Soil carbon formation is promoted by saturation deficit and existing mineral-associated carbon, not by microbial carbon-use efficiency",
    "doi": "10.1126/sciadv.adv9482",
    "url": "https://doi.org/10.1126/sciadv.adv9482",
    "journal": "Science Advances",
    "year": 2025,
    "authors": "King, A.; Sokol, N.",
    "abstract": "Mineral-associated organic carbon (MAOC) is the largest terrestrial pool of organic carbon, yet controls on its formation remain unresolved. Existing MAOC is thought to preclude additional C storage on minerals, but this perspective is difficult to reconcile with observations that MAOC stacks in multilayers, suggesting that existing MAOC could promote greater C retention. Here, in a manipulative experiment using 118 soils from 15 agricultural sites across the United States, we show that MAOC for",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Spatiotemporal toughness modulation in hydrogels through on-demand cross-linking",
    "doi": "10.1126/sciadv.adz0440",
    "url": "https://doi.org/10.1126/sciadv.adz0440",
    "journal": "Science Advances",
    "year": 2025,
    "authors": "Lee, J.; Castilho, R.; Nam, S.",
    "abstract": "\n            Tough hydrogels are promising for soft robotics, bioelectronics, and tissue adhesives due to their exceptional resilience and biocompatibility, yet precise spatiotemporal control of their mechanics remains challenging. Here, we present a hydrogel platform that enables spatiotemporal modulation of toughness through a latent ionic cross-linking mechanism. By embedding calcium carbonate (CaCO\n            3\n            ) microparticles in alginate/polyacrylamide double-network hydrogels",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Smart ultra-long-lasting sequentially triggerable and artfully implantable nozzle system for on-demand drug delivery for chronotherapy",
    "doi": "10.1126/sciadv.adv8734",
    "url": "https://doi.org/10.1126/sciadv.adv8734",
    "journal": "Science Advances",
    "year": 2025,
    "authors": "Zeng, Q.; Gong, Y.; Jiao, W.; Xu, J.; Chen, X.",
    "abstract": "\n            Conventional drug delivery methods for chronic disease often suffer from low potency and poor patient compliance, while current advanced devices face limitations because of bulkiness, frequent implantation needs, inflammation risk, and lack of precise control. To overcome these challenges, we developed the SUSTAIN—a smart, ultra-long-lasting, sequentially triggerable, and artfully implantable nozzle system. The SUSTAIN integrates an osmotic pressure–triggered module, an airflow-gene",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Experimental characterization of complex atmospheric flows: A wind turbine wake case study",
    "doi": "10.1126/sciadv.adw8524",
    "url": "https://doi.org/10.1126/sciadv.adw8524",
    "journal": "Science Advances",
    "year": 2025,
    "authors": "Angelou, N.; Sjöholm, M.; Mikkelsen, T.",
    "abstract": "Our current understanding of the interaction between the atmosphere and surface obstacles crucial for boundary-layer meteorology, forestry, urban climate, wind engineering, and wind energy is limited mainly to observations acquired in wind tunnel experiments and flow predictions from computational fluid dynamic models. Here, as a case study, we present spatially distributed measurements of a utility-scale wind turbine’s wake using three wind lidars that synchronously scan a volume of the atmosph",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Flash annealing–engineered wafer-scale relaxor antiferroelectrics for enhanced energy storage performance",
    "doi": "10.1126/sciadv.ady2349",
    "url": "https://doi.org/10.1126/sciadv.ady2349",
    "journal": "Science Advances",
    "year": 2025,
    "authors": "Li, Y.; Song, K.; Zhu, M.; Li, X.; Zeng, Z.",
    "abstract": "\n                    Dielectric capacitors are essential for energy storage systems because of their high-power density and fast operation speed. However, optimizing energy storage density with concurrent thermal stability remains a substantial challenge. Here, we develop a flash annealing process with ultrafast heating and cooling rates of 1000°C per second, which facilitates the rapid crystallization of PbZrO\n                    3\n                    film within a mere second, while locking it",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Atomic Sn–incorporated subnanopore-rich hard carbon host for highly reversible quasi-metallic Li storage",
    "doi": "10.1126/sciadv.ads6483",
    "url": "https://doi.org/10.1126/sciadv.ads6483",
    "journal": "Science Advances",
    "year": 2025,
    "authors": "Jin, T.; Zhang, X.; Yuan, S.; Yu, L.",
    "abstract": "\n            The practical application of Li metal anodes has been hindered by severely irreversible side reactions for low Coulombic efficiency, uncontrollable growth of Li dendrites, and large volume change. Herein, we report subnanopore-rich carbon spheres encapsulated with Sn single atoms (Sn/CS@SC) as a Li host to address these challenges. Owing to the high Li affinity of Sn single atoms, Sn/CS@SC can promote storage of quasi-metallic Li within the inner void space other than direct plating",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "The potential of wastewater treatment on carbon storage through ocean alkalinity enhancement",
    "doi": "10.1126/sciadv.ads0313",
    "url": "https://doi.org/10.1126/sciadv.ads0313",
    "journal": "Science Advances",
    "year": 2025,
    "authors": "Zheng, L.; Hu, Y.; Su, B.; Chen, Q.; Liu, J.",
    "abstract": "\n            Ocean alkalinity enhancement (OAE) implemented through wastewater treatment plants increases the alkalinity of the effluents and discharges them into the ocean, referred to as wastewater-based OAE. However, the alkalization capability and its carbon storage stability when adding alkaline minerals to wastewater treatment are uncertain. In this study, total alkalinity was enhanced to more than 10 millimoles per kilogram and phosphate removal was improved when we added olivine to waste",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "A compact cassette tape for DNA-based data storage",
    "doi": "10.1126/sciadv.ady3406",
    "url": "https://doi.org/10.1126/sciadv.ady3406",
    "journal": "Science Advances",
    "year": 2025,
    "authors": "Li, J.; Mao, C.; Wang, S.; Li, X.; Luo, X.",
    "abstract": "\n            DNA with high storage density can serve as an alternative storage medium to respond to the global explosion of data growth and become a powerful personal storage memory if an integrated compact device can store and handle large-scale data. Here, we incorporate a DNA cassette tape with 5.5 × 10\n            5\n            addressable data partitions (addressing rate up to 1570 partitions per second), a DNA loading capacity of 28.6 mg per kilometer, and deposit-many-recover-many (DMRM) ",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Extending tetrahedral network similarity to carbon: A type-I carbon clathrate stabilized by boron",
    "doi": "10.1126/sciadv.adv6867",
    "url": "https://doi.org/10.1126/sciadv.adv6867",
    "journal": "Science Advances",
    "year": 2025,
    "authors": "Strobel, T.; Bi, T.; Guńka, P.; Hansen, M.; Hübner, J.",
    "abstract": "\n            Clathrates are guest/host framework compounds composed of polyhedral cages, yet despite their prevalence among tetrahedral network formers, clathrates with a carbon host lattice remain unrealized synthetic targets. Here, we report a type-I carbon-based framework—a ubiquitous clathrate structure type found throughout compounds containing tetrahedral building blocks. Following a boron-stabilization scheme based on first-principles predictions in the Ca–B–C system at high pressure, typ",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "Carbon Trading & New Business Models",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "County-level intensity of carbon emissions from crop farming in China during 2000–2019",
    "doi": "10.1038/s41597-024-03296-y",
    "url": "https://doi.org/10.1038/s41597-024-03296-y",
    "journal": "Scientific Data",
    "year": 2024,
    "authors": "Li, C.; Jia, J.; Wu, F.; Zuo, L.; Cui, X.",
    "abstract": "AbstractAgriculture is an important contributor to global carbon emissions. With the implementation of the Sustainable Development Goals of the United Nations and China’s carbon neutral strategy, accurate estimation of carbon emissions from crop farming is essential to reduce agricultural carbon emissions and promote sustainable food production systems in China. However, previous long-term time series estimates in China have mainly focused on the national and provincial levels, which are insuffi",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Comprehensive Dataset on Electrical Load Profiles for Energy Community in Ireland",
    "doi": "10.1038/s41597-024-03454-2",
    "url": "https://doi.org/10.1038/s41597-024-03454-2",
    "journal": "Scientific Data",
    "year": 2024,
    "authors": "Trivedi, R.; Bahloul, M.; Saif, A.; Patra, S.; Khadem, S.",
    "abstract": "AbstractThis paper describes a comprehensive energy-related dataset, collected from residential electricity households within an energy community in Ireland, as part of StoreNet project. The data includes local weather parameters and per household power (W) and energy (Wh) components for various aspects such as active power consumption, PV generation, grid import and export, charging and discharging, and the state of charge of energy storage. Additionally, it provides weather data for the locati",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "The Plegma dataset: Domestic appliance-level and aggregate electricity demand with metadata from Greece",
    "doi": "10.1038/s41597-024-03208-0",
    "url": "https://doi.org/10.1038/s41597-024-03208-0",
    "journal": "Scientific Data",
    "year": 2024,
    "authors": "Athanasoulias, S.; Guasselli, F.; Doulamis, N.; Doulamis, A.; Ipiotis, N.",
    "abstract": "AbstractThe growing availability of smart meter data has facilitated the development of energy-saving services like demand response, personalized energy feedback, and non-intrusive-load-monitoring applications, all of which heavily rely on advanced machine learning algorithms trained on energy consumption datasets. To ensure the accuracy and reliability of these services, real-world smart meter data collection is crucial. The Plegma dataset described in this paper addresses this need bfy providi",
    "data_url": "",
    "source": "CrossRef",
    "direction": "FLEXERGY",
    "subcategory": "Demand Response",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "A unified dataset for pre-processed climate indicators weighted by gridded economic activity",
    "doi": "10.1038/s41597-024-03304-1",
    "url": "https://doi.org/10.1038/s41597-024-03304-1",
    "journal": "Scientific Data",
    "year": 2024,
    "authors": "Gortan, M.; Testa, L.; Fagiolo, G.; Lamperti, F.",
    "abstract": "AbstractAlthough high-resolution gridded climate variables are provided by multiple sources, the need for country and region-specific climate data weighted by indicators of economic activity is becoming increasingly common in environmental and economic research. We process available information from different climate data sources to provide spatially aggregated data with global coverage for both countries (GADM0 resolution) and regions (GADM1 resolution) and for a variety of climate indicators (",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Wind turbine condition monitoring dataset of Fraunhofer LBF",
    "doi": "10.1038/s41597-024-03934-5",
    "url": "https://doi.org/10.1038/s41597-024-03934-5",
    "journal": "Scientific Data",
    "year": 2024,
    "authors": "Mostafavi, A.; Friedmann, A.",
    "abstract": "AbstractFraunhofer wind turbine dataset contains monitoring data from a 750 W wind turbine, including accelerometers and tachometer, to capture structural response, bearing vibrations and rotational velocity. Additionally, temperatures, wind speed and wind direction have been measured, while weather conditions have been acquired from selected sources. Various damage scenarios, including mass imbalance, and aerodynamic imbalance as well as damages on bearings’ outer race, inner race and roller el",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "SDWPF: A Dataset for Spatial Dynamic Wind Power Forecasting over a Large Turbine Array",
    "doi": "10.1038/s41597-024-03427-5",
    "url": "https://doi.org/10.1038/s41597-024-03427-5",
    "journal": "Scientific Data",
    "year": 2024,
    "authors": "Zhou, J.; Lu, X.; Xiao, Y.; Tang, J.; Su, J.",
    "abstract": "AbstractWind power is a clean and renewable energy, yet it poses integration challenges to the grid due to its variable nature. Thus, Wind Power Forecasting (WPF) is crucial for its successful integration. However, existing WPF datasets often cover only a limited number of turbines and lack detailed information. To bridge this gap and advance WPF research, we introduce the Spatial Dynamic Wind Power Forecasting dataset (SDWPF). The SDWPF dataset not only provides information on power generation ",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "Carbon Trading & New Business Models",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Remotely sensed above-ground storage tank dataset for object detection and infrastructure assessment",
    "doi": "10.1038/s41597-023-02780-1",
    "url": "https://doi.org/10.1038/s41597-023-02780-1",
    "journal": "Scientific Data",
    "year": 2024,
    "authors": "Robinson, C.; Bradbury, K.; Borsuk, M.",
    "abstract": "AbstractRemotely sensed imagery has increased dramatically in quantity and public availability. However, automated, large-scale analysis of such imagery is hindered by a lack of the annotations necessary to train and test machine learning algorithms. In this study, we address this shortcoming with respect to above-ground storage tanks (ASTs) that are used in a wide variety of industries. We annotated available high-resolution, remotely sensed imagery to develop an original, publicly available mu",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "AI & Deep Learning"
  },
  {
    "title": "Assessing the emissions impact of grid-connected hydrogen production",
    "doi": "10.1038/s41560-023-01445-y",
    "url": "https://doi.org/10.1038/s41560-023-01445-y",
    "journal": "Nature Energy",
    "year": 2024,
    "authors": "",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "The influence of additionality and time-matching requirements on the emissions from grid-connected hydrogen production",
    "doi": "10.1038/s41560-023-01435-0",
    "url": "https://doi.org/10.1038/s41560-023-01435-0",
    "journal": "Nature Energy",
    "year": 2024,
    "authors": "Giovanniello, M.; Cybulsky, A.; Schittekatte, T.; Mallapragada, D.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Publisher Correction: The influence of additionality and time-matching requirements on the emissions from grid-connected hydrogen production",
    "doi": "10.1038/s41560-024-01475-0",
    "url": "https://doi.org/10.1038/s41560-024-01475-0",
    "journal": "Nature Energy",
    "year": 2024,
    "authors": "Giovanniello, M.; Cybulsky, A.; Schittekatte, T.; Mallapragada, D.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Weather-sensitive renewable energy sources do not subject power systems to blackouts",
    "doi": "10.1038/s41560-024-01657-w",
    "url": "https://doi.org/10.1038/s41560-024-01657-w",
    "journal": "Nature Energy",
    "year": 2024,
    "authors": "",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Impacts of renewable energy resources on the weather vulnerability of power systems",
    "doi": "10.1038/s41560-024-01652-1",
    "url": "https://doi.org/10.1038/s41560-024-01652-1",
    "journal": "Nature Energy",
    "year": 2024,
    "authors": "Zhao, J.; Li, F.; Zhang, Q.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Weather conditions linked to energy droughts in electricity systems with hydropower",
    "doi": "10.1038/s41560-024-01641-4",
    "url": "https://doi.org/10.1038/s41560-024-01641-4",
    "journal": "Nature Energy",
    "year": 2024,
    "authors": "",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Identifying the power-grid bottlenecks responsible for cascading failures during extreme storms",
    "doi": "10.1038/s41560-024-01499-6",
    "url": "https://doi.org/10.1038/s41560-024-01499-6",
    "journal": "Nature Energy",
    "year": 2024,
    "authors": "",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Increasing the resilience of the Texas power grid against extreme storms by hardening critical lines",
    "doi": "10.1038/s41560-023-01434-1",
    "url": "https://doi.org/10.1038/s41560-023-01434-1",
    "journal": "Nature Energy",
    "year": 2024,
    "authors": "Stürmer, J.; Plietzsch, A.; Vogt, T.; Hellmann, F.; Kurths, J.",
    "abstract": "AbstractThe Texas power grid on the Gulf Coast of the United States is frequently hit by tropical cyclones (TCs) causing widespread power outages, a risk that is expected to substantially increase under global warming. Here we introduce a new approach that combines a probabilistic line failure model with a network model of the Texas grid to simulate the spatio-temporal co-evolution of wind-induced failures of high-voltage transmission lines and the resulting cascading power outages from seven ma",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Artificial intelligence-aided wind plant optimization for nationwide evaluation of land use and economic benefits of wake steering",
    "doi": "10.1038/s41560-024-01516-8",
    "url": "https://doi.org/10.1038/s41560-024-01516-8",
    "journal": "Nature Energy",
    "year": 2024,
    "authors": "Harrison-Atlas, D.; Glaws, A.; King, R.; Lantz, E.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Machine learning-accelerated discovery of heat-resistant polysulfates for electrostatic energy storage",
    "doi": "10.1038/s41560-024-01670-z",
    "url": "https://doi.org/10.1038/s41560-024-01670-z",
    "journal": "Nature Energy",
    "year": 2024,
    "authors": "Li, H.; Zheng, H.; Yue, T.; Xie, Z.; Yu, S.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "AI & Deep Learning"
  },
  {
    "title": "Accurate nowcasting of cloud cover at solar photovoltaic plants using geostationary satellite images",
    "doi": "10.1038/s41467-023-44666-1",
    "url": "https://doi.org/10.1038/s41467-023-44666-1",
    "journal": "Nature Communications",
    "year": 2024,
    "authors": "Xia, P.; Zhang, L.; Min, M.; Li, J.; Wang, Y.",
    "abstract": "Abstract\n                  Accurate nowcasting for cloud fraction is still intractable challenge for stable solar photovoltaic electricity generation. By combining continuous radiance images measured by geostationary satellite and an advanced recurrent neural network, we develop a nowcasting algorithm for predicting cloud fraction at the leading time of 0–4 h at photovoltaic plants. Based on this algorithm, a cyclically updated prediction system is also established and tested at five photovoltai",
    "data_url": "",
    "source": "CrossRef",
    "direction": "CleanTech",
    "subcategory": "Solar PV & Storage",
    "direction_label": "Novel Low/Zero Carbon Technologies",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "A multi-demand operating system underlying diverse cognitive tasks",
    "doi": "10.1038/s41467-024-46511-5",
    "url": "https://doi.org/10.1038/s41467-024-46511-5",
    "journal": "Nature Communications",
    "year": 2024,
    "authors": "Cai, W.; Taghia, J.; Menon, V.",
    "abstract": "AbstractThe existence of a multiple-demand cortical system with an adaptive, domain-general, role in cognition has been proposed, but the underlying dynamic mechanisms and their links to cognitive control abilities are poorly understood. Here we use a probabilistic generative Bayesian model of brain circuit dynamics to determine dynamic brain states across multiple cognitive domains, independent datasets, and participant groups, including task fMRI data from Human Connectome Project, Dual Mechan",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "Carbon Trading & New Business Models",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Unlocking the potential of biogas systems for energy production and climate solutions in rural communities",
    "doi": "10.1038/s41467-024-50091-9",
    "url": "https://doi.org/10.1038/s41467-024-50091-9",
    "journal": "Nature Communications",
    "year": 2024,
    "authors": "Luo, T.; Shen, B.; Mei, Z.; Hove, A.; Ju, K.",
    "abstract": "Abstract\n                  On-site conversion of organic waste into biogas to satisfy consumer energy demand has the potential to realize energy equality and mitigate climate change reliably. However, existing methods ignore either real-time full supply or methane escape when supply and demand are mismatched. Here, we show an improved design of community biogas production and distribution system to overcome these and achieve full co-benefits in developing economies. We take five existing systems",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Demand Response & IoT"
  },
  {
    "title": "Altered grid-like coding in early blind people",
    "doi": "10.1038/s41467-024-47747-x",
    "url": "https://doi.org/10.1038/s41467-024-47747-x",
    "journal": "Nature Communications",
    "year": 2024,
    "authors": "Sigismondi, F.; Xu, Y.; Silvestri, M.; Bottini, R.",
    "abstract": "Abstract\n                  Cognitive maps in the hippocampal-entorhinal system are central for the representation of both spatial and non-spatial relationships. Although this system, especially in humans, heavily relies on vision, the role of visual experience in shaping the development of cognitive maps remains largely unknown. Here, we test sighted and early blind individuals in both imagined navigation in fMRI and real-world navigation. During imagined navigation, the Human Navigation Network",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "How grid reinforcement costs differ by the income of electric vehicle users",
    "doi": "10.1038/s41467-024-53644-0",
    "url": "https://doi.org/10.1038/s41467-024-53644-0",
    "journal": "Nature Communications",
    "year": 2024,
    "authors": "Steinbach, S.; Blaschke, M.",
    "abstract": "AbstractThe simultaneous charging of many electric vehicles in future mobility scenarios may lead to peaks and overloads threatening grid stability. The necessary infrastructure investments vary by the number and model type of vehicles driven and the residents’ charging preferences. These attributes significantly depend on socio-economic factors such as income. Using power flow simulations based on real-life driving profiles, we predict massive cost asymmetries with an investment demand up to 33",
    "data_url": "",
    "source": "CrossRef",
    "direction": "FLEXERGY",
    "subcategory": "Electric Vehicles & Mobility",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Uncovering 2-D toroidal representations in grid cell ensemble activity during 1-D behavior",
    "doi": "10.1038/s41467-024-49703-1",
    "url": "https://doi.org/10.1038/s41467-024-49703-1",
    "journal": "Nature Communications",
    "year": 2024,
    "authors": "Hermansen, E.; Klindt, D.; Dunn, B.",
    "abstract": "Abstract\n                  Minimal experiments, such as head-fixed wheel-running and sleep, offer experimental advantages but restrict the amount of observable behavior, making it difficult to classify functional cell types. Arguably, the grid cell, and its striking periodicity, would not have been discovered without the perspective provided by free behavior in an open environment. Here, we show that by shifting the focus from single neurons to populations, we change the minimal experimental com",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "On the potential of vehicle-to-grid and second-life batteries to provide energy and material security",
    "doi": "10.1038/s41467-024-48554-0",
    "url": "https://doi.org/10.1038/s41467-024-48554-0",
    "journal": "Nature Communications",
    "year": 2024,
    "authors": "Aguilar Lopez, F.; Lauinger, D.; Vuille, F.; Müller, D.",
    "abstract": "AbstractThe global energy transition relies increasingly on lithium-ion batteries for electric transportation and renewable energy integration. Given the highly concentrated supply chain of battery materials, importing regions have a strategic imperative to reduce their reliance on battery material imports through, e.g., battery recycling or reuse. We investigate the potential of vehicle-to-grid and second-life batteries to reduce resource use by displacing new stationary batteries dedicated to ",
    "data_url": "",
    "source": "CrossRef",
    "direction": "FLEXERGY",
    "subcategory": "Electric Vehicles & Mobility",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Grid-like entorhinal representation of an abstract value space during prospective decision making",
    "doi": "10.1038/s41467-024-45127-z",
    "url": "https://doi.org/10.1038/s41467-024-45127-z",
    "journal": "Nature Communications",
    "year": 2024,
    "authors": "Nitsch, A.; Garvert, M.; Bellmund, J.; Schuck, N.; Doeller, C.",
    "abstract": "Abstract\n                  How valuable a choice option is often changes over time, making the prediction of value changes an important challenge for decision making. Prior studies identified a cognitive map in the hippocampal-entorhinal system that encodes relationships between states and enables prediction of future states, but does not inherently convey value during prospective decision making. In this fMRI study, participants predicted changing values of choice options in a sequence, forming",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "The value of long-duration energy storage under various grid conditions in a zero-emissions future",
    "doi": "10.1038/s41467-024-53274-6",
    "url": "https://doi.org/10.1038/s41467-024-53274-6",
    "journal": "Nature Communications",
    "year": 2024,
    "authors": "Staadecker, M.; Szinai, J.; Sánchez-Pérez, P.; Kurtz, S.; Hidalgo-Gonzalez, P.",
    "abstract": "Abstract\n                  \n                    Long-duration energy storage (LDES) is a key resource in enabling zero-emissions electricity grids but its role within different types of grids is not well understood. Using the Switch capacity expansion model, we model a zero-emissions Western Interconnect with high geographical resolution to understand the value of LDES under 39 scenarios with different generation mixes, transmission expansion, storage costs, and storage mandates. We find that a)",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Renewable Energy Simulation Tools",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Electric vehicle battery chemistry affects supply chain disruption vulnerabilities",
    "doi": "10.1038/s41467-024-46418-1",
    "url": "https://doi.org/10.1038/s41467-024-46418-1",
    "journal": "Nature Communications",
    "year": 2024,
    "authors": "Cheng, A.; Fuchs, E.; Karplus, V.; Michalek, J.",
    "abstract": "Abstract\n          We examine the relationship between electric vehicle battery chemistry and supply chain disruption vulnerability for four critical minerals: lithium, cobalt, nickel, and manganese. We compare the nickel manganese cobalt (NMC) and lithium iron phosphate (LFP) cathode chemistries by (1) mapping the supply chains for these four materials, (2) calculating a vulnerability index for each cathode chemistry for various focal countries and (3) using network flow optimization to bound u",
    "data_url": "",
    "source": "CrossRef",
    "direction": "FLEXERGY",
    "subcategory": "Electric Vehicles & Mobility",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Offshore wind and wave energy can reduce total installed capacity required in zero-emissions grids",
    "doi": "10.1038/s41467-024-50040-6",
    "url": "https://doi.org/10.1038/s41467-024-50040-6",
    "journal": "Nature Communications",
    "year": 2024,
    "authors": "Gonzalez, N.; Serna-Torre, P.; Sánchez-Pérez, P.; Davidson, R.; Murray, B.",
    "abstract": "Abstract\n                  As the world races to decarbonize power systems to mitigate climate change, the body of research analyzing paths to zero emissions electricity grids has substantially grown. Although studies typically include commercially available technologies, few of them consider offshore wind and wave energy as contenders in future zero-emissions grids. Here, we model with high geographic resolution both offshore wind and wave energy as independent technologies with the possibility",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Renewable Energy Resource Mapping",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Optimal blade pitch control for enhanced vertical-axis wind turbine performance",
    "doi": "10.1038/s41467-024-46988-0",
    "url": "https://doi.org/10.1038/s41467-024-46988-0",
    "journal": "Nature Communications",
    "year": 2024,
    "authors": "Le Fouest, S.; Mulleners, K.",
    "abstract": "Abstract\n                  Vertical-axis wind turbines are great candidates to enable wind power extraction in urban and off-shore applications. Currently, concerns around turbine efficiency and structural integrity limit their industrial deployment. Flow control can mitigate these concerns. Here, we experimentally demonstrate the potential of individual blade pitching as a control strategy and explain the flow physics that yields the performance enhancement. We perform automated experiments usi",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Effect of adaptive cruise control on fuel consumption in real-world driving conditions",
    "doi": "10.1038/s41467-024-54066-8",
    "url": "https://doi.org/10.1038/s41467-024-54066-8",
    "journal": "Nature Communications",
    "year": 2024,
    "authors": "Moawad, A.; Zebiak, M.; Han, J.; Karbowski, D.; Zhang, Y.",
    "abstract": "Abstract\n                  This paper presents a comprehensive analysis of the impact of adaptive cruise control on energy consumption in real-world driving conditions based on a natural experiment: a large-scale observational dataset of driving data from a diverse fleet of vehicles and drivers. The analysis is conducted at two different fidelity levels: (1) a macroscopic trip-level benefit estimate that compares trips with and without cruise control in a counterfactual way using statistical met",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "The role of electric grid research in addressing climate change",
    "doi": "10.1038/s41558-024-02092-1",
    "url": "https://doi.org/10.1038/s41558-024-02092-1",
    "journal": "Nature Climate Change",
    "year": 2024,
    "authors": "Xie, L.; Majumder, S.; Huang, T.; Zhang, Q.; Chang, P.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "The role of advanced nuclear reactors and fuel cycles in a future energy system",
    "doi": "10.1093/pnasnexus/pgae030",
    "url": "https://doi.org/10.1093/pnasnexus/pgae030",
    "journal": "npj Clean Energy",
    "year": 2024,
    "authors": "Kornecki, K.; Wise, C.",
    "abstract": "Abstract\n               Nuclear power has been an important part of the US electricity system since the 1950s and continues to be a major source of low-carbon electricity today. Despite having low emissions, high grid reliability, and an excellent track record of safety, nuclear power also demands significant time and upfront capital to deploy, can struggle to compete economically with other generation sources, has intrinsic proliferation risk by relying on fissile material for fuel, and generat",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Storm and tidal interactions control sediment exchange in mixed-energy coastal systems",
    "doi": "10.1093/pnasnexus/pgae042",
    "url": "https://doi.org/10.1093/pnasnexus/pgae042",
    "journal": "npj Clean Energy",
    "year": 2024,
    "authors": "Georgiou, I.; FitzGerald, D.; Hanegan, K.",
    "abstract": "Abstract\n               Storms can have devasting effects on shorelines, causing flooding and the destruction of property and infrastructure. As global warming and the frequency and magnitude of tropical storms increase, barrier islands comprising 10% of the world's coast may undergo significant change caused by beach erosion, loss of dunes, and formation of washovers and tidal inlets. Understanding how storms affect sediment transport at tidal inlets is an understudied subject that directly inf",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "Carbon Trading & New Business Models",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Effective data-driven collective variables for free energy calculations from metadynamics of paths",
    "doi": "10.1093/pnasnexus/pgae159",
    "url": "https://doi.org/10.1093/pnasnexus/pgae159",
    "journal": "npj Clean Energy",
    "year": 2024,
    "authors": "Müllender, L.; Rizzi, A.; Parrinello, M.; Carloni, P.; Mandelli, D.",
    "abstract": "Abstract\n               A variety of enhanced sampling (ES) methods predict multidimensional free energy landscapes associated with biological and other molecular processes as a function of a few selected collective variables (CVs). The accuracy of these methods is crucially dependent on the ability of the chosen CVs to capture the relevant slow degrees of freedom of the system. For complex processes, finding such CVs is the real challenge. Machine learning (ML) CVs offer, in principle, a soluti",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "AI & Deep Learning"
  },
  {
    "title": "Microphase iron particle growth promoted by solar wind implantation in lunar soils",
    "doi": "10.1093/pnasnexus/pgae450",
    "url": "https://doi.org/10.1093/pnasnexus/pgae450",
    "journal": "npj Clean Energy",
    "year": 2024,
    "authors": "Lu, X.; Chen, J.; Cao, H.; Fu, X.; Zeng, X.",
    "abstract": "Abstract\n               Lunar soils record the history and spectral changes resulting from the space-weathering process. The solar wind and micrometeoroids are the main space-weathering agents leading to darkening (decreasing albedo) and reddening (increasing reflectance with longer wavelength) of visible and near-infrared spectra. Nevertheless, their relative contributions are not well constrained and understood. In this study, we examine the near-infrared spectral variation as a function of lu",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "Novel Low/Zero Carbon Technologies",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Food–energy–water nexus optimization brings substantial reduction of urban resource consumption and greenhouse gas emissions",
    "doi": "10.1093/pnasnexus/pgae028",
    "url": "https://doi.org/10.1093/pnasnexus/pgae028",
    "journal": "npj Clean Energy",
    "year": 2024,
    "authors": "Zhang, P.; Zhang, L.; Hao, Y.; Xu, M.; Pang, M.",
    "abstract": "Abstract\n               Urban sustainability is a key to achieving the UN sustainable development goals (SDGs). Secure and efficient provision of food, energy, and water (FEW) resources is a critical strategy for urban sustainability. While there has been extensive discussion on the positive effects of the FEW nexus on resource efficiency and climate impacts, measuring the extent to which such synergy can benefit urban sustainability remains challenging. Here, we have developed a systematic and ",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Prioritizing social vulnerability in urban heat mitigation",
    "doi": "10.1093/pnasnexus/pgae360",
    "url": "https://doi.org/10.1093/pnasnexus/pgae360",
    "journal": "npj Clean Energy",
    "year": 2024,
    "authors": "Fung, K.; Yang, Z.; Martilli, A.; Krayenhoff, E.; Niyogi, D.",
    "abstract": "Abstract\n               We utilized city-scale simulations to quantitatively compare the diverse urban overheating mitigation strategies, specifically tied to social vulnerability and their cooling efficacies during heatwaves. We enhanced the Weather Research and Forecasting model to encompass the urban tree effect and calculate the Universal Thermal Climate Index for assessing thermal comfort. Taking Houston, Texas, and United States as an example, the study reveals that equitably mitigating ur",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "Novel Low/Zero Carbon Technologies",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Robust capital cost optimization of generation and multitimescale storage requirements for a 100% renewable Australian electricity grid",
    "doi": "10.1093/pnasnexus/pgae127",
    "url": "https://doi.org/10.1093/pnasnexus/pgae127",
    "journal": "npj Clean Energy",
    "year": 2024,
    "authors": "Shaikh, R.; Vowles, D.; Dinovitser, A.; Allison, A.; Abbott, D.",
    "abstract": "Abstract\n               Transitioning from a fossil-fuel-dependent economy to one based on renewable energy requires significant investment and technological advancement. While wind and solar technologies provide lower cost electricity, enhanced energy storage and transmission infrastructure come at a cost for managing renewable intermittency. Energy storage systems vary in characteristics and costs, and future grids will incorporate multiple technologies, yet the optimal combination of storage ",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "Novel Low/Zero Carbon Technologies",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Cocaine diminishes functional network robustness and destabilizes the energy landscape of neuronal activity in the medial prefrontal cortex",
    "doi": "10.1093/pnasnexus/pgae092",
    "url": "https://doi.org/10.1093/pnasnexus/pgae092",
    "journal": "npj Clean Energy",
    "year": 2024,
    "authors": "Borzou, A.; Miller, S.; Hommel, J.; Schwarz, J.",
    "abstract": "Abstract\n               We present analysis of neuronal activity recordings from a subset of neurons in the medial prefrontal cortex of rats before and after the administration of cocaine. Using an underlying modern Hopfield model as a description for the neuronal network, combined with a machine learning approach, we compute the underlying functional connectivity of the neuronal network. We find that the functional connectivity changes after the administration of cocaine with both functional-ex",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "AI & Deep Learning"
  },
  {
    "title": "A biospecies-derived genomic DNA hybrid gel electrolyte for electrochemical energy storage",
    "doi": "10.1093/pnasnexus/pgae213",
    "url": "https://doi.org/10.1093/pnasnexus/pgae213",
    "journal": "npj Clean Energy",
    "year": 2024,
    "authors": "Mitta, S.; Kim, J.; Rana, H.; Kokkiligadda, S.; Lim, Y.",
    "abstract": "Abstract\n               Intrinsic impediments, namely weak mechanical strength, low ionic conductivity, low electrochemical performance, and stability have largely inhibited beyond practical applications of hydrogels in electronic devices and remains as a significant challenge in the scientific world. Here, we report a biospecies-derived genomic DNA hybrid gel electrolyte with many synergistic effects, including robust mechanical properties (mechanical strength and elongation of 6.98 MPa and 997",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "China's progress in synergetic governance of climate change and multiple environmental issues",
    "doi": "10.1093/pnasnexus/pgae351",
    "url": "https://doi.org/10.1093/pnasnexus/pgae351",
    "journal": "npj Clean Energy",
    "year": 2024,
    "authors": "Yang, J.; Zhao, Z.; Fang, W.; Ma, Z.; Liu, M.",
    "abstract": "Abstract\n               Advancing the synergetic control of climate change and environmental crisis is crucial for achieving global sustainable development goals. This study evaluates synergetic governance levels over climate change and four environmental issues at the provincial level in China from 2009 to 2020. Our findings reveal significant progress in China's coordinated efforts to mitigate carbon emissions, reduce air pollutants, and conserve water resources. However, there remains room fo",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Catch the wind: Optimizing wind turbine power generation by addressing wind veer effects",
    "doi": "10.1093/pnasnexus/pgae480",
    "url": "https://doi.org/10.1093/pnasnexus/pgae480",
    "journal": "npj Clean Energy",
    "year": 2024,
    "authors": "Gao, L.; Milliren, C.; Dasari, T.; Knoll, A.; Hong, J.",
    "abstract": "Abstract\n               Wind direction variability with height, known as “wind veer,” results in power losses for wind turbines (WTs) that rely on single-point wind measurements at the turbine nacelles. To address this challenge, we introduce a yaw control strategy designed to optimize turbine alignment by adjusting the yaw angle based on specific wind veer conditions, thereby boosting power generation efficiency. This strategy integrates modest yaw offset angles into the existing turbine contro",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Deciphering the variability in air-sea gas transfer due to sea state and wind history",
    "doi": "10.1093/pnasnexus/pgae389",
    "url": "https://doi.org/10.1093/pnasnexus/pgae389",
    "journal": "npj Clean Energy",
    "year": 2024,
    "authors": "Yang, M.; Moffat, D.; Dong, Y.; Bidlot, J.",
    "abstract": "Abstract\n               Understanding processes driving air-sea gas transfer and being able to model both its mean and variability are critical for studies of climate and carbon cycle. The air-sea gas transfer velocity (K660) is almost universally parameterized as a function of wind speed in large scale models—an oversimplification that buries the mechanisms controlling K660 and neglects much natural variability. Sea state has long been speculated to affect gas transfer, but consistent relations",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Enhancing molecular oxygen activation by nitrogen-doped carbon encapsulating FeNi alloys with ultra-low Pt loading",
    "doi": "10.1093/pnasnexus/pgae594",
    "url": "https://doi.org/10.1093/pnasnexus/pgae594",
    "journal": "npj Clean Energy",
    "year": 2024,
    "authors": "Zhu, D.; Huang, Y.; Shi, X.; Li, R.; Wang, Z.",
    "abstract": "Abstract\n               Modulating the electronic structure of noble metals via electronic metal–support interaction (EMSI) has been proven effectively for facilitating molecular oxygen activation and catalytic oxidation reactions. Nevertheless, the investigation of the fundamental mechanisms underlying activity enhancement has primarily focused on metal oxides as supports, especially in the catalytic degradation of volatile organic compounds. In this study, a novel Pt catalyst supported on nitr",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "An integrated experimental–modeling approach to identify key processes for carbon mineralization in fractured mafic and ultramafic rocks",
    "doi": "10.1093/pnasnexus/pgae388",
    "url": "https://doi.org/10.1093/pnasnexus/pgae388",
    "journal": "npj Clean Energy",
    "year": 2024,
    "authors": "Neil, C.; Yang, Y.; Nisbet, H.; Iyare, U.; Boampong, L.",
    "abstract": "Abstract\n               Controlling atmospheric warming requires immediate reduction of carbon dioxide (CO2) emissions, as well as the active removal and sequestration of CO2 from current point sources. One promising proposed strategy to reduce atmospheric CO2 levels is geologic carbon sequestration (GCS), where CO2 is injected into the subsurface and reacts with the formation to precipitate carbonate minerals. Rapid mineralization has recently been reported for field tests in mafic and ultramaf",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "Novel Low/Zero Carbon Technologies",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Optimising the manufacturing of a β-Ti alloy produced via direct energy deposition using small dataset machine learning",
    "doi": "10.1038/s41598-024-57498-w",
    "url": "https://doi.org/10.1038/s41598-024-57498-w",
    "journal": "Scientific Reports",
    "year": 2024,
    "authors": "Brooke, R.; Qiu, D.; Le, T.; Gibson, M.; Zhang, D.",
    "abstract": "Abstract\n                  \n                    Successful additive manufacturing involves the optimisation of numerous process parameters that significantly influence product quality and manufacturing success. One commonly used criteria based on a collection of parameters is the global energy distribution (GED). This parameter encapsulates the energy input onto the surface of a build, and is a function of the laser power, laser scanning speed and laser spot size. This study uses machine learnin",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "AI & Deep Learning"
  },
  {
    "title": "Leveraging opposition-based learning for solar photovoltaic model parameter estimation with exponential distribution optimization algorithm",
    "doi": "10.1038/s41598-023-50890-y",
    "url": "https://doi.org/10.1038/s41598-023-50890-y",
    "journal": "Scientific Reports",
    "year": 2024,
    "authors": "Kullampalayam Murugaiyan, N.; Chandrasekaran, K.; Manoharan, P.; Derebew, B.",
    "abstract": "AbstractGiven the multi-model and nonlinear characteristics of photovoltaic (PV) models, parameter extraction presents a challenging problem. This challenge is exacerbated by the propensity of conventional algorithms to get trapped in local optima due to the complex nature of the problem. Accurate parameter estimation, nonetheless, is crucial due to its significant impact on the PV system’s performance, influencing both current and energy production. While traditional methods have provided reaso",
    "data_url": "",
    "source": "CrossRef",
    "direction": "CleanTech",
    "subcategory": "Solar PV & Storage",
    "direction_label": "Novel Low/Zero Carbon Technologies",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "MPPT control of photovoltaic array based on improved marine predator algorithm under complex solar irradiance conditions",
    "doi": "10.1038/s41598-024-70811-x",
    "url": "https://doi.org/10.1038/s41598-024-70811-x",
    "journal": "Scientific Reports",
    "year": 2024,
    "authors": "Zhang, H.; Wang, X.; Zhang, J.; Ge, Y.; Wang, L.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "CleanTech",
    "subcategory": "Solar PV & Storage",
    "direction_label": "Novel Low/Zero Carbon Technologies",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Enhanced Whale optimization algorithms for parameter identification of solar photovoltaic cell models: a comparative study",
    "doi": "10.1038/s41598-024-67600-x",
    "url": "https://doi.org/10.1038/s41598-024-67600-x",
    "journal": "Scientific Reports",
    "year": 2024,
    "authors": "Yang, S.; Xiong, G.; Fu, X.; Mirjalili, S.; Mohamed, A.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "CleanTech",
    "subcategory": "Solar PV & Storage",
    "direction_label": "Novel Low/Zero Carbon Technologies",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "An improved transient search optimization algorithm for building energy optimization and hybrid energy sizing applications",
    "doi": "10.1038/s41598-024-68239-4",
    "url": "https://doi.org/10.1038/s41598-024-68239-4",
    "journal": "Scientific Reports",
    "year": 2024,
    "authors": "Jearsiripongkul, T.; Karbasforoushha, M.; Khajehzadeh, M.; Keawsawasvong, S.; Thongchom, C.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Optimization of building integrated energy scheduling using an improved genetic whale algorithm",
    "doi": "10.1038/s41598-024-52995-4",
    "url": "https://doi.org/10.1038/s41598-024-52995-4",
    "journal": "Scientific Reports",
    "year": 2024,
    "authors": "Wei, L.; An, G.",
    "abstract": "AbstractRenewable energy generation has become the general trend with increasing environmental problems. However, the instability of renewable energy generation and the diversification of user demand are highlighted and the optimization of energy scheduling has become the key to solve the problem. This study introduces an energy scheduling optimization model tailored for building integrated energy systems, encompassing elements like gas turbines, wind and solar modules, ground source heat pumps,",
    "data_url": "",
    "source": "CrossRef",
    "direction": "CleanTech",
    "subcategory": "Solar PV & Storage",
    "direction_label": "Novel Low/Zero Carbon Technologies",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Comparative analysis of direct coupling and MPPT control in standalone PV systems for solar energy optimization to meet sustainable building energy demands",
    "doi": "10.1038/s41598-024-72606-6",
    "url": "https://doi.org/10.1038/s41598-024-72606-6",
    "journal": "Scientific Reports",
    "year": 2024,
    "authors": "Nataraj, C.; Karthikeyan, G.; Bharathi, G.; Duraikannan, S.",
    "abstract": "Abstract\n                  Solar energy, a prominent renewable source, has reached an installed capacity of 71.78 GW in India. This research explored the load demands of the computer center at an engineering college in Tanjore, Tamil Nadu, India. The computer center at the engineering college has an annual energy requirement of 260,552 kWh/Year. Consequently, the research focused on the planning and implementation of a standalone photovoltaic (SAPV) system, assessing it against the institution's",
    "data_url": "",
    "source": "CrossRef",
    "direction": "CleanTech",
    "subcategory": "Solar PV & Storage",
    "direction_label": "Novel Low/Zero Carbon Technologies",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Multiple objective energy optimization of a trade center building based on genetic algorithm using ecological materials",
    "doi": "10.1038/s41598-024-58515-8",
    "url": "https://doi.org/10.1038/s41598-024-58515-8",
    "journal": "Scientific Reports",
    "year": 2024,
    "authors": "Kabiri, E.; Maftouni, N.",
    "abstract": "AbstractIt is crucial to optimize energy consumption in buildings while considering thermal comfort. The first step here involved an EnergyPlus simulation on a trade center building located in Tehran, Bandar Abbas, and Tabriz, Iran. A multi-objective optimization was then performed based on non-dominated sorting genetic algorithm II (NSGA-II) in jEPlus + EA to establish the building in the selected city where would benefit the most from implementing the radiant ceiling cooling system. Efforts we",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "Novel Low/Zero Carbon Technologies",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Active learning-based machine learning approach for enhancing environmental sustainability in green building energy consumption",
    "doi": "10.1038/s41598-024-70729-4",
    "url": "https://doi.org/10.1038/s41598-024-70729-4",
    "journal": "Scientific Reports",
    "year": 2024,
    "authors": "Mahmood, S.; Sun, H.; Ali Alhussan, A.; Iqbal, A.; El-kenawy, E.",
    "abstract": "AbstractGreen building (GB) techniques are essential for reducing energy waste in the construction sector, which accounts for almost 40% of global energy consumption. Despite their importance, challenges such as occupant behavior and energy management gaps often result in GBs consuming up to 2.5 times more energy than intended. To address this, Building Automation Systems (BAS) play a crucial role in enhancing energy efficiency. This research develops a predictive model for GB design using machi",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "AI & Deep Learning"
  },
  {
    "title": "Developing the hybrid BIM-BEM and jellyfish search optimization system for optimizing energy consumption and building installation costs",
    "doi": "10.1038/s41598-024-68021-6",
    "url": "https://doi.org/10.1038/s41598-024-68021-6",
    "journal": "Scientific Reports",
    "year": 2024,
    "authors": "Luong, D.; Truong, N.; Ngo, N.; Nguyen, N.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Urban Water-Energy consumption Prediction Influenced by Climate Change utilizing an innovative deep learning method",
    "doi": "10.1038/s41598-024-81836-7",
    "url": "https://doi.org/10.1038/s41598-024-81836-7",
    "journal": "Scientific Reports",
    "year": 2024,
    "authors": "Wang, D.; Zhang, Y.; Yousefi, N.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Robust load-frequency control of islanded urban microgrid using 1PD-3DOF-PID controller including mobile EV energy storage",
    "doi": "10.1038/s41598-024-64794-y",
    "url": "https://doi.org/10.1038/s41598-024-64794-y",
    "journal": "Scientific Reports",
    "year": 2024,
    "authors": "Davoudkhani, I.; Zare, P.; Abdelaziz, A.; Bajaj, M.; Tuka, M.",
    "abstract": "AbstractElectricity generation in Islanded Urban Microgrids (IUMG) now relies heavily on a diverse range of Renewable Energy Sources (RES). However, the dependable utilization of these sources hinges upon efficient Electrical Energy Storage Systems (EESs). As the intermittent nature of RES output and the low inertia of IUMGs often lead to significant frequency fluctuations, the role of EESs becomes pivotal. While these storage systems effectively mitigate frequency deviations, their high costs a",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "Carbon Trading & New Business Models",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Research on coupling optimization of carbon emissions and carbon leakage in international construction projects",
    "doi": "10.1038/s41598-024-59531-4",
    "url": "https://doi.org/10.1038/s41598-024-59531-4",
    "journal": "Scientific Reports",
    "year": 2024,
    "authors": "Zhou, Z.; Wang, Y.; Alcalá, J.; Yepes, V.",
    "abstract": "AbstractDue to the rapid economic development of globalization and the intensification of economic and trade exchanges, cross-international and regional carbon emissions have become increasingly severe. Governments worldwide establish laws and regulations to protect their countries' environmental impact. Therefore, selecting robustness evaluation models and metrics is an urgent research topic. This article proves the reliability and scientific of the assessment data through literature coupling e",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "A rule-based energy management system for hybrid renewable energy sources with battery bank optimized by genetic algorithm optimization",
    "doi": "10.1038/s41598-024-54333-0",
    "url": "https://doi.org/10.1038/s41598-024-54333-0",
    "journal": "Scientific Reports",
    "year": 2024,
    "authors": "Jamal, S.; Pasupuleti, J.; Ekanayake, J.",
    "abstract": "AbstractA Nanogrid (NG) model is described as a power distribution system that integrates Hybrid Renewable Energy Sources (HRESs) and Energy Storage Systems (ESSs) into the primary grid. However, this process is affected by several factors, like load variability, market pricing, and the intermittent nature of Wind Turbines (WTs) and Photovoltaic (PV) systems. Hence, other researchers in the past have used a few optimization-based processes to improve the development of Energy Management Systems ",
    "data_url": "",
    "source": "CrossRef",
    "direction": "CleanTech",
    "subcategory": "Solar PV & Storage",
    "direction_label": "Novel Low/Zero Carbon Technologies",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "A novel advanced hybrid fuzzy MPPT controllers for renewable energy systems",
    "doi": "10.1038/s41598-024-72060-4",
    "url": "https://doi.org/10.1038/s41598-024-72060-4",
    "journal": "Scientific Reports",
    "year": 2024,
    "authors": "Rafi Kiran, S.; Alsaif, F.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Forecasting for electricity demand utilizing enhanced inception-V4 using improved Osprey optimization",
    "doi": "10.1038/s41598-024-81487-8",
    "url": "https://doi.org/10.1038/s41598-024-81487-8",
    "journal": "Scientific Reports",
    "year": 2024,
    "authors": "Chen, S.; Fang, X.; Khayatnezhad, M.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Optimization clearing strategy for multi-region electricity-heat market considering shared energy storage and integrated demand response",
    "doi": "10.1038/s41598-024-72397-w",
    "url": "https://doi.org/10.1038/s41598-024-72397-w",
    "journal": "Scientific Reports",
    "year": 2024,
    "authors": "Chen, S.; Ye, Z.; Meng, Y.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "FLEXERGY",
    "subcategory": "Demand Response",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Deep learning-based forecasting of electricity consumption",
    "doi": "10.1038/s41598-024-56602-4",
    "url": "https://doi.org/10.1038/s41598-024-56602-4",
    "journal": "Scientific Reports",
    "year": 2024,
    "authors": "Qureshi, M.; Arbab, M.; Rehman, S.",
    "abstract": "AbstractBuilding energy management systems (BEMS) are integrated computerized systems that track and manage the energy use of many pieces of building-related machinery and equipment, including lighting, power systems, and HVAC systems. Modern buildings must have BEMSs in order to reduce energy usage while maintaining comfort. Not only for energy-saving purposes, BEMS is essential in enhancing the quality of the energy supply, which helps to gain a better understanding of how energy is used and t",
    "data_url": "",
    "source": "CrossRef",
    "direction": "FLEXERGY",
    "subcategory": "Smart Home & EMS",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Estimating the energy consumption for residential buildings in semiarid and arid desert climate using artificial intelligence",
    "doi": "10.1038/s41598-024-63843-w",
    "url": "https://doi.org/10.1038/s41598-024-63843-w",
    "journal": "Scientific Reports",
    "year": 2024,
    "authors": "Wefki, H.; Khallaf, R.; Ebid, A.",
    "abstract": "AbstractThis research aims to develop predictive models to estimate building energy accurately. Three commonly used artificial intelligence techniques were chosen to develop a new building energy estimation model. The chosen techniques are Genetic Programming (GP), Artificial Neural Network (ANN), and Evolutionary Polynomial Regression (EPR). Sixteen energy efficiency measures were collected and used in designing and evaluating the proposed models, which include building dimensions, orientation,",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "AI & Deep Learning"
  },
  {
    "title": "Deep learning-driven hybrid model for short-term load forecasting and smart grid information management",
    "doi": "10.1038/s41598-024-63262-x",
    "url": "https://doi.org/10.1038/s41598-024-63262-x",
    "journal": "Scientific Reports",
    "year": 2024,
    "authors": "Wen, X.; Liao, J.; Niu, Q.; Shen, N.; Bao, Y.",
    "abstract": "AbstractAccurate power load forecasting is crucial for the sustainable operation of smart grids. However, the complexity and uncertainty of load, along with the large-scale and high-dimensional energy information, present challenges in handling intricate dynamic features and long-term dependencies. This paper proposes a computational approach to address these challenges in short-term power load forecasting and energy information management, with the goal of accurately predicting future load dema",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Construction of power network security risk assessment model based on LSA-SVM algorithm in the background of smart grid",
    "doi": "10.1038/s41598-024-59473-x",
    "url": "https://doi.org/10.1038/s41598-024-59473-x",
    "journal": "Scientific Reports",
    "year": 2024,
    "authors": "Qi, H.; Zhu, W.; Ye, M.; Hu, Y.; Wang, Y.",
    "abstract": "AbstractDue to theintricate and interdependent nature of the smart grid, it has encountered an increasing number of security threats in recent years. Currently, conventional security measures such as firewalls, intrusion detection, and malicious detection technologies offer specific protection based on their unique perspectives. However, as the types and concealment of attacksincrease, these measures struggle to detect them promptly and respond accordingly. In order to meet the social demand for",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Machine learning optimization for hybrid electric vehicle charging in renewable microgrids",
    "doi": "10.1038/s41598-024-63775-5",
    "url": "https://doi.org/10.1038/s41598-024-63775-5",
    "journal": "Scientific Reports",
    "year": 2024,
    "authors": "Hassan, M.",
    "abstract": "AbstractRenewable microgrids enhance security, reliability, and power quality in power systems by integrating solar and wind sources, reducing greenhouse gas emissions. This paper proposes a machine learning approach, leveraging Gaussian Process (GP) and Krill Herd Algorithm (KHA), for energy management in renewable microgrids with a reconfigurable structure based on remote switching of tie and sectionalizing. The method utilizes Gaussian Process (GP) for modeling hybrid electric vehicle (HEV) c",
    "data_url": "",
    "source": "CrossRef",
    "direction": "FLEXERGY",
    "subcategory": "Electric Vehicles & Mobility",
    "direction_label": "Novel Low/Zero Carbon Technologies",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Electric vehicle path optimization research based on charging and switching methods under V2G",
    "doi": "10.1038/s41598-024-81449-0",
    "url": "https://doi.org/10.1038/s41598-024-81449-0",
    "journal": "Scientific Reports",
    "year": 2024,
    "authors": "Liu, H.; Zhang, A.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "FLEXERGY",
    "subcategory": "Electric Vehicles & Mobility",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Managing grid impacts from increased electric vehicle adoption in African cities",
    "doi": "10.1038/s41598-024-75039-3",
    "url": "https://doi.org/10.1038/s41598-024-75039-3",
    "journal": "Scientific Reports",
    "year": 2024,
    "authors": "Lukuyu, J.; Shirley, R.; Taneja, J.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "FLEXERGY",
    "subcategory": "Electric Vehicles & Mobility",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Power consumption prediction for electric vehicle charging stations and forecasting income",
    "doi": "10.1038/s41598-024-56507-2",
    "url": "https://doi.org/10.1038/s41598-024-56507-2",
    "journal": "Scientific Reports",
    "year": 2024,
    "authors": "Akshay, K.; Grace, G.; Gunasekaran, K.; Samikannu, R.",
    "abstract": "AbstractElectric vehicles (EVs) are the future of the automobile industry, as they produce zero emissions and address environmental and health concerns caused by traditional fuel-poared vehicles. As more people shift towards EVs, the demand for power consumption forecasting is increasing to manage the charging stations effectively. Predicting power consumption can help optimize operations, prevent grid overloading, and power outages, and assist companies in estimating the number of charging stat",
    "data_url": "",
    "source": "CrossRef",
    "direction": "FLEXERGY",
    "subcategory": "Electric Vehicles & Mobility",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Dynamic performance of rotor-side nonlinear control technique for doubly-fed multi-rotor wind energy based on improved super-twisting algorithms under variable wind speed",
    "doi": "10.1038/s41598-024-55271-7",
    "url": "https://doi.org/10.1038/s41598-024-55271-7",
    "journal": "Scientific Reports",
    "year": 2024,
    "authors": "Benbouhenni, H.; Yessef, M.; Colak, I.; Bizon, N.; Kotb, H.",
    "abstract": "AbstractThe paper proposes a nonlinear controller called dual super-twisting sliding mode command (DSTSMC) for controlling and regulating the rotor side converter (RSC) of multi-rotor wind power systems that use doubly-fed induction generators. It was proposed that this controller be developed as an alternative to the direct power control (DPC), which makes use of a pulse width modulation (PWM) strategy to regulate the RSC's functioning. Overcoming the power/current quality issue with the propos",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Continuous input current buck DC/DC converter for small-size wind energy systems featuring current sensorless MPPT control",
    "doi": "10.1038/s41598-023-50692-2",
    "url": "https://doi.org/10.1038/s41598-023-50692-2",
    "journal": "Scientific Reports",
    "year": 2024,
    "authors": "Zakzouk, N.",
    "abstract": "AbstractFor decentralized electrification in remote areas, small-sized wind energy systems (WESs) are considered sustainable and affordable solution when employing an efficient, small-sized component converter integrated with a less-sophisticated, cost-effective MPPT controller. Unfortunately, using a conventional buck DC/DC converter as a MPP tracker suffer from input current discontinuity. The latter results in high ripples in the tracked rectified wind power which reduces the captured power a",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Effective dynamic energy management algorithm for grid-interactive microgrid with hybrid energy storage system",
    "doi": "10.1038/s41598-024-70599-w",
    "url": "https://doi.org/10.1038/s41598-024-70599-w",
    "journal": "Scientific Reports",
    "year": 2024,
    "authors": "Kamagaté, Y.; Shah, H.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "Carbon Trading & New Business Models",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Optimal scheduling model using the IGDT method for park integrated energy systems considering P2G–CCS and cloud energy storage",
    "doi": "10.1038/s41598-024-68292-z",
    "url": "https://doi.org/10.1038/s41598-024-68292-z",
    "journal": "Scientific Reports",
    "year": 2024,
    "authors": "Wang, L.; Cheng, J.; Luo, X.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Digital finance reduces urban carbon footprint pressure in 277 Chinese cities",
    "doi": "10.1038/s41598-024-67315-z",
    "url": "https://doi.org/10.1038/s41598-024-67315-z",
    "journal": "Scientific Reports",
    "year": 2024,
    "authors": "Dong, Z.; Yao, S.",
    "abstract": "AbstractAs global warming's impact on humanity surpasses initial predictions, numerous countries confront heightened risks associated with escalating urban carbon footprints. Concurrently, digital finance has flourished, propelled by advancements in digital technology. This convergence underscores the urgency of exploring digital finance's role in mitigating urban carbon footprint pressures. This study analyzes data spanning 277 Chinese cities from 2011 to 2020, yielding several key findings: Fi",
    "data_url": "",
    "source": "CrossRef",
    "direction": "EnergiTrade",
    "subcategory": "Urban Carbon Footprint",
    "direction_label": "Carbon Trading & New Business Models",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Short-term multi-energy consumption forecasting for integrated energy system based on interactive multi-scale convolutional module",
    "doi": "10.1038/s41598-024-72103-w",
    "url": "https://doi.org/10.1038/s41598-024-72103-w",
    "journal": "Scientific Reports",
    "year": 2024,
    "authors": "Liu, F.; Huang, Y.; Wang, Y.; Xia, E.; Qureshi, H.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Blockchain-based energy consumption approaches in IoT",
    "doi": "10.1038/s41598-024-77792-x",
    "url": "https://doi.org/10.1038/s41598-024-77792-x",
    "journal": "Scientific Reports",
    "year": 2024,
    "authors": "Habibullah, S.; Alam, S.; Ghosh, S.; Dey, A.; De, A.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Demand Response & IoT"
  },
  {
    "title": "Optimization scheduling of microgrid comprehensive demand response load considering user satisfaction",
    "doi": "10.1038/s41598-024-66492-1",
    "url": "https://doi.org/10.1038/s41598-024-66492-1",
    "journal": "Scientific Reports",
    "year": 2024,
    "authors": "Wang, C.; Li, X.",
    "abstract": "AbstractThe original load control model of microgrid based on demand response lacks the factors of incentive demand response, the overall satisfaction of users is low, the degree of demand response is low, the Time Of Use (TOU) price of peak-valley filling capacity is weak, and the peak-valley difference of load curve is large. Regarding the limitations of the current microgrid demand response model, this study further optimizes the flexible load control strategy and proposes a two-objective opt",
    "data_url": "",
    "source": "CrossRef",
    "direction": "FLEXERGY",
    "subcategory": "Demand Response",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "A Multi-Layer Techno-Economic-Environmental Energy Management Optimization in Cooperative Multi-Microgrids with Demand Response Program and Uncertainties Consideration",
    "doi": "10.1038/s41598-024-72706-3",
    "url": "https://doi.org/10.1038/s41598-024-72706-3",
    "journal": "Scientific Reports",
    "year": 2024,
    "authors": "Alamir, N.; Kamel, S.; Megahed, T.; Hori, M.; Abdelkader, S.",
    "abstract": "AbstractThis paper presents a multi-layer, multi-objective (MLMO) optimization model for techno-economic-environmental energy management in cooperative multi-Microgrids (MMGs) that incorporates a Demand Response Program (DRP). The proposed MLMO approach simultaneously optimizes operating costs, MMG operator benefits, environmental emissions, and MMG dependency. This paper proposed a new hybrid ε-lexicography–weighted-sum that eliminates the need to normalize or scalarize objectives. The first la",
    "data_url": "",
    "source": "CrossRef",
    "direction": "FLEXERGY",
    "subcategory": "Demand Response",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Optimizing demand response and load balancing in smart EV charging networks using AI integrated blockchain framework",
    "doi": "10.1038/s41598-024-82257-2",
    "url": "https://doi.org/10.1038/s41598-024-82257-2",
    "journal": "Scientific Reports",
    "year": 2024,
    "authors": "Singh, A.; Kumar, R.; Madhavi, K.; Alsaif, F.; Bajaj, M.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "FLEXERGY",
    "subcategory": "Electric Vehicles & Mobility",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "The evolving long tail at the edge of the grid: Benefits and concerns",
    "doi": "10.1016/j.joule.2024.04.005",
    "url": "https://doi.org/10.1016/j.joule.2024.04.005",
    "journal": "Joule",
    "year": 2024,
    "authors": "Parag, Y.; Zemah-Shamir, S.; Shaviv, E.; Teschner, N.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Foundation models for the electric power grid",
    "doi": "10.1016/j.joule.2024.11.002",
    "url": "https://doi.org/10.1016/j.joule.2024.11.002",
    "journal": "Joule",
    "year": 2024,
    "authors": "Hamann, H.; Gjorgiev, B.; Brunschwiler, T.; Martins, L.; Puech, A.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "An empirical agent-based model of consumer co-adoption of low-carbon technologies to inform energy policy",
    "doi": "10.1016/j.crsus.2024.100268",
    "url": "https://doi.org/10.1016/j.crsus.2024.100268",
    "journal": "Cell Reports Sustainability",
    "year": 2024,
    "authors": "van der Kam, M.; Lagomarsino, M.; Azar, E.; Hahnel, U.; Parra, D.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Demand Response & IoT"
  },
  {
    "title": "Seasonal hydrogen energy storage sizing: Two-stage economic-safety optimization for integrated energy systems in northwest China",
    "doi": "10.1016/j.isci.2024.110691",
    "url": "https://doi.org/10.1016/j.isci.2024.110691",
    "journal": "iScience",
    "year": 2024,
    "authors": "Li, L.; Sun, Y.; Han, Y.; Chen, W.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Many-objective bi-level energy scheduling method for integrated energy stations based on power allocation strategy",
    "doi": "10.1016/j.isci.2024.109305",
    "url": "https://doi.org/10.1016/j.isci.2024.109305",
    "journal": "iScience",
    "year": 2024,
    "authors": "Liao, X.; Ma, J.; Yin, B.; Qian, B.; Lei, R.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "On the optimization of the interconnection of photovoltaic modules integrated in vehicles",
    "doi": "10.1016/j.isci.2024.110089",
    "url": "https://doi.org/10.1016/j.isci.2024.110089",
    "journal": "iScience",
    "year": 2024,
    "authors": "Macías, J.; Herrero, R.; San José, L.; Núñez, R.; Antón, I.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "CleanTech",
    "subcategory": "Solar PV & Storage",
    "direction_label": "Novel Low/Zero Carbon Technologies",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Batteries or hydrogen or both for grid electricity storage upon full electrification of 145 countries with wind-water-solar?",
    "doi": "10.1016/j.isci.2024.108988",
    "url": "https://doi.org/10.1016/j.isci.2024.108988",
    "journal": "iScience",
    "year": 2024,
    "authors": "Jacobson, M.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "Novel Low/Zero Carbon Technologies",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Optimal scheduling of electricity and hydrogen integrated energy system considering multiple uncertainties",
    "doi": "10.1016/j.isci.2024.109654",
    "url": "https://doi.org/10.1016/j.isci.2024.109654",
    "journal": "iScience",
    "year": 2024,
    "authors": "Chang, P.; Li, C.; Zhu, Q.; Zhu, T.; Shi, J.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "The carbon footprint of predicting CO2 storage capacity in metal-organic frameworks within neural networks",
    "doi": "10.1016/j.isci.2024.109644",
    "url": "https://doi.org/10.1016/j.isci.2024.109644",
    "journal": "iScience",
    "year": 2024,
    "authors": "Korolev, V.; Mitrofanov, A.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "Carbon Trading & New Business Models",
    "refined_category": "AI & Deep Learning"
  },
  {
    "title": "Enhancing the European power system resilience with a recommendation system for voluntary demand response",
    "doi": "10.1016/j.isci.2024.111430",
    "url": "https://doi.org/10.1016/j.isci.2024.111430",
    "journal": "iScience",
    "year": 2024,
    "authors": "Silva, C.; Bessa, R.; Andrade, J.; Coelho, F.; Costa, R.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "FLEXERGY",
    "subcategory": "Demand Response",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "Demand Response & IoT"
  },
  {
    "title": "PM\n                    <sub>2.5</sub>\n                    exposure disparities persist despite strict vehicle emissions controls in California",
    "doi": "10.1126/sciadv.adn8544",
    "url": "https://doi.org/10.1126/sciadv.adn8544",
    "journal": "Science Advances",
    "year": 2024,
    "authors": "Koolik, L.; Alvarado, Á.; Budahn, A.; Plummer, L.; Marshall, J.",
    "abstract": "\n                    As policymakers increasingly focus on environmental justice, a key question is whether emissions reductions aimed at addressing air quality or climate change can also ameliorate persistent air pollution exposure disparities. We examine evidence from California’s aggressive vehicle emissions control policy from 2000 to 2019. We find a 65% reduction in modeled statewide average exposure to PM\n                    2.5\n                    from on-road vehicles, yet for people of ",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Physics-guided deep learning for skillful wind-wave modeling",
    "doi": "10.1126/sciadv.adr3559",
    "url": "https://doi.org/10.1126/sciadv.adr3559",
    "journal": "Science Advances",
    "year": 2024,
    "authors": "Wang, X.; Jiang, H.",
    "abstract": "Modeling sea surface wind-waves is crucial for both scientific research and engineering applications. Nowadays, the most accurate wave models are based on numerical methods, which primarily concern the wave spectrum evolution by solving wave action balance partial differential equations. These methods are computationally expensive and limited by incomplete physical representations of wave spectral evolution. Here, we present a deep learning–based wave model trained using observation-merged wave ",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "AI & Deep Learning"
  },
  {
    "title": "Nonsynchronous rotation of icy moon ice shells: The thermal wind perspective",
    "doi": "10.1126/sciadv.adk2277",
    "url": "https://doi.org/10.1126/sciadv.adk2277",
    "journal": "Science Advances",
    "year": 2024,
    "authors": "Kang, W.",
    "abstract": "The ice shells of icy satellites have been hypothesized to undergo nonsynchronous rotation (NSR) under the influence of tidal torques and/or ocean currents. In this work, the author proposes that the thermal wind relationship can be combined with geostrophic turbulence theory to predict ocean stress onto the ice shell inside the tangent cylinder. High-resolution numerical simulations validate the prediction within a factor of 2. For the prediction to be valid, the rotation effect must dominate (",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "CO\n            <sub>2</sub>\n            capture, geological storage, and mineralization using biobased biodegradable chelating agents and seawater",
    "doi": "10.1126/sciadv.adq0515",
    "url": "https://doi.org/10.1126/sciadv.adq0515",
    "journal": "Science Advances",
    "year": 2024,
    "authors": "Wang, J.; Sekiai, R.; Tamura, R.; Watanabe, N.",
    "abstract": "\n            Geological storage and mineralization of CO\n            2\n            in mafic/ultramafic reservoirs faces challenges including limited effective porosity, permeability, and rock reactivity; difficulties in using seawater for CO\n            2\n            capture; and uncontrolled carbonation. This study introduces a CO\n            2\n            capture, storage, and mineralization approach with the utilization of biobased biodegradable chelating agents and seawater. An acidic chelat",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Olivine alteration and the loss of Mars’ early atmospheric carbon",
    "doi": "10.1126/sciadv.adm8443",
    "url": "https://doi.org/10.1126/sciadv.adm8443",
    "journal": "Science Advances",
    "year": 2024,
    "authors": "Murray, J.; Jagoutz, O.",
    "abstract": "\n            The early Martian atmosphere had 0.25 to 4 bar of CO\n            2\n            but thinned rapidly around 3.5 billion years ago. The fate of that carbon remains poorly constrained. The hydrothermal alteration of ultramafic rocks, rich in Fe(II) and Mg, forms both abiotic methane, serpentine, and high-surface-area smectite clays. Given the abundance of ultramafic rocks and smectite in the Martian upper crust and the growing evidence of organic carbon in Martian sedimentary rocks, we ",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Demand Response & IoT"
  },
  {
    "title": "A crowdsourced dataset of aerial images with annotated solar photovoltaic arrays and installation metadata",
    "doi": "10.1038/s41597-023-01951-4",
    "url": "https://doi.org/10.1038/s41597-023-01951-4",
    "journal": "Scientific Data",
    "year": 2023,
    "authors": "Kasmi, G.; Saint-Drenan, Y.; Trebosc, D.; Jolivet, R.; Leloux, J.",
    "abstract": "AbstractPhotovoltaic (PV) energy generation plays a crucial role in the energy transition. Small-scale, rooftop PV installations are deployed at an unprecedented pace, and their safe integration into the grid requires up-to-date, high-quality information. Overhead imagery is increasingly being used to improve the knowledge of rooftop PV installations with machine learning models capable of automatically mapping these installations. However, these models cannot be reliably transferred from one re",
    "data_url": "",
    "source": "CrossRef",
    "direction": "CleanTech",
    "subcategory": "Solar PV & Storage",
    "direction_label": "Novel Low/Zero Carbon Technologies",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Solar active region magnetogram image dataset for studies of space weather",
    "doi": "10.1038/s41597-023-02628-8",
    "url": "https://doi.org/10.1038/s41597-023-02628-8",
    "journal": "Scientific Data",
    "year": 2023,
    "authors": "Boucheron, L.; Vincent, T.; Grajeda, J.; Wuest, E.",
    "abstract": "AbstractIn this dataset we provide a comprehensive collection of line-of-sight (LOS) solar photospheric magnetograms (images quantifying the strength of the photospheric magnetic field) from the National Aeronautics and Space Administration’s (NASA’s) Solar Dynamics Observatory (SDO). The dataset incorporates data from three sources and provides SDO Helioseismic and Magnetic Imager (HMI) magnetograms of solar active regions (regions of large magnetic flux, generally the source of eruptive events",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "Novel Low/Zero Carbon Technologies",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "A benchmark dataset for binary segmentation and quantification of dust emissions from unsealed roads",
    "doi": "10.1038/s41597-022-01918-x",
    "url": "https://doi.org/10.1038/s41597-022-01918-x",
    "journal": "Scientific Data",
    "year": 2023,
    "authors": "De Silva, A.; Ranasinghe, R.; Sounthararajah, A.; Haghighi, H.; Kodikara, J.",
    "abstract": "AbstractThe generation of reference data for machine learning models is challenging for dust emissions due to perpetually dynamic environmental conditions. We generated a new vision dataset with the goal of advancing semantic segmentation to identify and quantify vehicle-induced dust clouds from images. We conducted field experiments on 10 unsealed road segments with different types of road surface materials in varying climatic conditions to capture vehicle-induced road dust. A direct single-len",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "AI & Deep Learning"
  },
  {
    "title": "A Dataset for Electricity Market Studies on Western and Northeastern Power Grids in the United States",
    "doi": "10.1038/s41597-023-02448-w",
    "url": "https://doi.org/10.1038/s41597-023-02448-w",
    "journal": "Scientific Data",
    "year": 2023,
    "authors": "Zhang, Q.; Li, F.",
    "abstract": "AbstractEfficient electricity market operations and cost-effective electricity generations are fundamental to a low-carbon energy future. The Western Electricity Coordinating Council (WECC) and Northeast Power Coordinating Council (NPCC) systems were built to provide efficient electrical grid simulation solutions for their respective U.S. regions. Data reuse for electricity economic studies remains a challenge due to the lack of credible and realistic economic data. This paper delivers a compreh",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "Carbon Trading & New Business Models",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "A synthetic dataset of Danish residential electricity prosumers",
    "doi": "10.1038/s41597-023-02271-3",
    "url": "https://doi.org/10.1038/s41597-023-02271-3",
    "journal": "Scientific Data",
    "year": 2023,
    "authors": "Yuan, R.; Pourmousavi, S.; Soong, W.; Black, A.; Liisberg, J.",
    "abstract": "AbstractConventional residential electricity consumers are becoming prosumers who not only consume electricity but also produce it. This shift is expected to occur over the next few decades at a large scale, and it presents numerous uncertainties and risks for the operation, planning, investment, and viable business models of the electricity grid. To prepare for this shift, researchers, utilities, policymakers, and emerging businesses require a comprehensive understanding of future prosumers’ el",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "A 21-year dataset (2000–2020) of gap-free global daily surface soil moisture at 1-km grid resolution",
    "doi": "10.1038/s41597-023-01991-w",
    "url": "https://doi.org/10.1038/s41597-023-01991-w",
    "journal": "Scientific Data",
    "year": 2023,
    "authors": "Zheng, C.; Jia, L.; Zhao, T.",
    "abstract": "AbstractGlobal soil moisture estimates from current satellite missions are suffering from inherent discontinuous observations and coarse spatial resolution, which limit applications especially at the fine spatial scale. This study developed a dataset of global gap-free surface soil moisture (SSM) at daily 1-km resolution from 2000 to 2020. This is achieved based on the European Space Agency - Climate Change Initiative (ESA-CCI) SSM combined product at 0.25° resolution. Firstly, an operational ga",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Positron emission tomography dataset of [11C]carbon dioxide storage in coal for geo-sequestration application",
    "doi": "10.1038/s41597-023-02754-3",
    "url": "https://doi.org/10.1038/s41597-023-02754-3",
    "journal": "Scientific Data",
    "year": 2023,
    "authors": "Jing, Y.; Kumaran, A.; Stimson, D.; Mardon, K.; Najdovski, L.",
    "abstract": "AbstractPositron Emission Tomography (PET) imaging has demonstrated its capability in providing time-lapse fluid flow visualisation for improving the understanding of flow properties of geologic media. To investigate the process of CO2 geo-sequestration using PET imaging technology, [11C]CO2 is the most optimal and direct radiotracer. However, it has not been extensively used due to the short half-life of Carbon-11 (20.4 minutes). In this work, a novel laboratory protocol is developed to use [11",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Demand Response & IoT"
  },
  {
    "title": "Taking control of energy as a solar prosumer",
    "doi": "10.1038/s41560-022-01174-8",
    "url": "https://doi.org/10.1038/s41560-022-01174-8",
    "journal": "Nature Energy",
    "year": 2023,
    "authors": "Middlemiss, L.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "Novel Low/Zero Carbon Technologies",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Emissions savings from equitable energy demand reduction",
    "doi": "10.1038/s41560-023-01283-y",
    "url": "https://doi.org/10.1038/s41560-023-01283-y",
    "journal": "Nature Energy",
    "year": 2023,
    "authors": "Büchs, M.; Cass, N.; Mullen, C.; Lucas, K.; Ivanova, D.",
    "abstract": "AbstractEnergy demand reduction (EDR) will be required to reach climate targets in the Global North. To be compatible with just transitions principles, EDR needs to be equitable. Equitable EDR may involve targeting high energy users while ensuring the satisfaction of needs for all, which could require increasing consumption of low users. Emissions impacts of equitable EDR approaches have not yet been assessed. This Article finds that capping energy use of the top quintile of consumers across 27 ",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "Carbon Trading & New Business Models",
    "refined_category": "Demand Response & IoT"
  },
  {
    "title": "A global model of hourly space heating and cooling demand at multiple spatial scales",
    "doi": "10.1038/s41560-023-01341-5",
    "url": "https://doi.org/10.1038/s41560-023-01341-5",
    "journal": "Nature Energy",
    "year": 2023,
    "authors": "Staffell, I.; Pfenninger, S.; Johnson, N.",
    "abstract": "Abstract\n                  \n                    Accurate modelling of the weather’s temporal and spatial impacts on building energy demand is critical to decarbonizing energy systems. Here we introduce a customizable model for hourly heating and cooling demand applicable globally at all spatial scales. We validate against demand from ~5,000 buildings and 43 regions across four continents. The model requires limited data inputs and shows better agreement with measured demand than existing models.",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Rail-based mobile energy storage as a grid-reliability solution for climate extremes",
    "doi": "10.1038/s41560-023-01284-x",
    "url": "https://doi.org/10.1038/s41560-023-01284-x",
    "journal": "Nature Energy",
    "year": 2023,
    "authors": "",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Leveraging rail-based mobile energy storage to increase grid reliability in the face of climate uncertainty",
    "doi": "10.1038/s41560-023-01276-x",
    "url": "https://doi.org/10.1038/s41560-023-01276-x",
    "journal": "Nature Energy",
    "year": 2023,
    "authors": "Moraski, J.; Popovich, N.; Phadke, A.",
    "abstract": "Abstract\n                  Maintaining reliability is increasingly challenging for electric grids as they endure more frequent extreme weather events and utilize more intermittent generation. Exploration of alternative reliability approaches is needed to effectively address these emerging issues. Here we examine the potential to use the US rail system as a nationwide backup transmission grid over which containerized batteries, or rail-based mobile energy storage (RMES), are shared among regions ",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "International relations theory on grid communities and international politics in a green world",
    "doi": "10.1038/s41560-023-01363-z",
    "url": "https://doi.org/10.1038/s41560-023-01363-z",
    "journal": "Nature Energy",
    "year": 2023,
    "authors": "Smith Stegen, K.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Local and utility-wide cost allocations for a more equitable wildfire-resilient distribution grid",
    "doi": "10.1038/s41560-023-01306-8",
    "url": "https://doi.org/10.1038/s41560-023-01306-8",
    "journal": "Nature Energy",
    "year": 2023,
    "authors": "Wang, Z.; Wara, M.; Majumdar, A.; Rajagopal, R.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "A phenazine-based high-capacity and high-stability electrochemical CO2 capture cell with coupled electricity storage",
    "doi": "10.1038/s41560-023-01347-z",
    "url": "https://doi.org/10.1038/s41560-023-01347-z",
    "journal": "Nature Energy",
    "year": 2023,
    "authors": "Pang, S.; Jin, S.; Yang, F.; Alberts, M.; Li, L.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Stability follows efficiency based on the analysis of a large perovskite solar cells ageing dataset",
    "doi": "10.1038/s41467-023-40585-3",
    "url": "https://doi.org/10.1038/s41467-023-40585-3",
    "journal": "Nature Communications",
    "year": 2023,
    "authors": "Hartono, N.; Köbler, H.; Graniero, P.; Khenkin, M.; Schlatmann, R.",
    "abstract": "AbstractWhile perovskite solar cells have reached competitive efficiency values during the last decade, stability issues remain a critical challenge to be addressed for pushing this technology towards commercialisation. In this study, we analyse a large homogeneous dataset of Maximum Power Point Tracking (MPPT) operational ageing data that we collected with a custom-built High-throughput Ageing System in the past 3 years. In total, 2,245 MPPT ageing curves are analysed which were obtained under ",
    "data_url": "",
    "source": "CrossRef",
    "direction": "CleanTech",
    "subcategory": "Solar PV & Storage",
    "direction_label": "Novel Low/Zero Carbon Technologies",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Creating complex protocells and prototissues using simple DNA building blocks",
    "doi": "10.1038/s41467-023-36875-5",
    "url": "https://doi.org/10.1038/s41467-023-36875-5",
    "journal": "Nature Communications",
    "year": 2023,
    "authors": "Arulkumaran, N.; Singer, M.; Howorka, S.; Burns, J.",
    "abstract": "AbstractBuilding synthetic protocells and prototissues hinges on the formation of biomimetic skeletal frameworks. Recreating the complexity of cytoskeletal and exoskeletal fibers, with their widely varying dimensions, cellular locations and functions, represents a major material hurdle and intellectual challenge which is compounded by the additional demand of using simple building blocks to ease fabrication and control. Here we harness simplicity to create complexity by assembling structural fra",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Global green hydrogen-based steel opportunities surrounding high quality renewable energy and iron ore deposits",
    "doi": "10.1038/s41467-023-38123-2",
    "url": "https://doi.org/10.1038/s41467-023-38123-2",
    "journal": "Nature Communications",
    "year": 2023,
    "authors": "Devlin, A.; Kossen, J.; Goldie-Jones, H.; Yang, A.",
    "abstract": "AbstractThe steel sector currently accounts for 7% of global energy-related CO2 emissions and requires deep reform to disconnect from fossil fuels. Here, we investigate the market competitiveness of one of the widely considered decarbonisation routes for primary steel production: green hydrogen-based direct reduction of iron ore followed by electric arc furnace steelmaking. Through analysing over 300 locations by combined use of optimisation and machine learning, we show that competitive renewab",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "AI & Deep Learning"
  },
  {
    "title": "The asymmetric effects of climate risk on higher-moment connectedness among carbon, energy and metals markets",
    "doi": "10.1038/s41467-023-42925-9",
    "url": "https://doi.org/10.1038/s41467-023-42925-9",
    "journal": "Nature Communications",
    "year": 2023,
    "authors": "Zhou, Y.; Wu, S.; Liu, Z.; Rognone, L.",
    "abstract": "AbstractClimate change affects price fluctuations in the carbon, energy and metals markets through physical and transition risks. Climate physical risk is mainly caused by extreme weather, natural disasters and other events caused by climate change, whereas climate transition risk mainly results from the gradual switchover to a low-carbon economy. Given that the connectedness between financial markets may be affected by various factors such as extreme events and economic transformation, understa",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "Carbon Trading & New Business Models",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Geospatial mapping of distribution grid with machine learning and publicly-accessible multi-modal data",
    "doi": "10.1038/s41467-023-39647-3",
    "url": "https://doi.org/10.1038/s41467-023-39647-3",
    "journal": "Nature Communications",
    "year": 2023,
    "authors": "Wang, Z.; Majumdar, A.; Rajagopal, R.",
    "abstract": "AbstractDetailed and location-aware distribution grid information is a prerequisite for various power system applications such as renewable energy integration, wildfire risk assessment, and infrastructure planning. However, a generalizable and scalable approach to obtain such information is still lacking. In this work, we develop a machine-learning-based framework to map both overhead and underground distribution grids using widely-available multi-modal data including street view images, road ne",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Mental search of concepts is supported by egocentric vector representations and restructured grid maps",
    "doi": "10.1038/s41467-023-43831-w",
    "url": "https://doi.org/10.1038/s41467-023-43831-w",
    "journal": "Nature Communications",
    "year": 2023,
    "authors": "Viganò, S.; Bayramova, R.; Doeller, C.; Bottini, R.",
    "abstract": "Abstract\n                  The human hippocampal-entorhinal system is known to represent both spatial locations and abstract concepts in memory in the form of allocentric cognitive maps. Using fMRI, we show that the human parietal cortex evokes complementary egocentric representations in conceptual spaces during goal-directed mental search, akin to those observable during physical navigation to determine where a goal is located relative to oneself (e.g., to our left or to our right). Concurrentl",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Grid integration feasibility and investment planning of offshore wind power under carbon-neutral transition in China",
    "doi": "10.1038/s41467-023-37536-3",
    "url": "https://doi.org/10.1038/s41467-023-37536-3",
    "journal": "Nature Communications",
    "year": 2023,
    "authors": "Guo, X.; Chen, X.; Chen, X.; Sherman, P.; Wen, J.",
    "abstract": "AbstractOffshore wind power, with accelerated declining levelized costs, is emerging as a critical building-block to fully decarbonize the world’s largest CO2 emitter, China. However, system integration barriers as well as system balancing costs have not been quantified yet. Here we develop a bottom-up model to test the grid accommodation capabilities and design the optimal investment plans for offshore wind power considering resource distributions, hourly power system simulations, and transmiss",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Renewable Energy Resource Mapping",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Electric vehicle batteries alone could satisfy short-term grid storage demand by as early as 2030",
    "doi": "10.1038/s41467-022-35393-0",
    "url": "https://doi.org/10.1038/s41467-022-35393-0",
    "journal": "Nature Communications",
    "year": 2023,
    "authors": "Xu, C.; Behrens, P.; Gasper, P.; Smith, K.; Hu, M.",
    "abstract": "AbstractThe energy transition will require a rapid deployment of renewable energy (RE) and electric vehicles (EVs) where other transit modes are unavailable. EV batteries could complement RE generation by providing short-term grid services. However, estimating the market opportunity requires an understanding of many socio-technical parameters and constraints. We quantify the global EV battery capacity available for grid storage using an integrated model incorporating future EV battery deployment",
    "data_url": "",
    "source": "CrossRef",
    "direction": "FLEXERGY",
    "subcategory": "Electric Vehicles & Mobility",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Incentive based emergency demand response effectively reduces peak load during heatwave without harm to vulnerable groups",
    "doi": "10.1038/s41467-023-41970-8",
    "url": "https://doi.org/10.1038/s41467-023-41970-8",
    "journal": "Nature Communications",
    "year": 2023,
    "authors": "Wang, Z.; Lu, B.; Wang, B.; Qiu, Y.; Shi, H.",
    "abstract": "Abstract\n                  The incentive-based emergency demand response measure serves as an important regulatory tool during energy system operations. However, whether people will sacrifice comfort to respond to it during heatwave and what the effect on heat vulnerable populations will be are still unclear. A large-scale emergency demand response pilot involving 205,129 households was conducted in southwestern China during continuous extreme high temperatures in summer. We found that the incen",
    "data_url": "",
    "source": "CrossRef",
    "direction": "FLEXERGY",
    "subcategory": "Demand Response",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Switchable biomimetic nanochannels for on-demand SO2 detection by light-controlled photochromism",
    "doi": "10.1038/s41467-023-37654-y",
    "url": "https://doi.org/10.1038/s41467-023-37654-y",
    "journal": "Nature Communications",
    "year": 2023,
    "authors": "Zhang, D.; Sun, Y.; Wang, Z.; Liu, F.; Zhang, X.",
    "abstract": "AbstractIn contrast to the conventional passive reaction to analytes, here, we create a proof-of-concept nanochannel system capable of on-demand recognition of the target to achieve an unbiased response. Inspired by light-activatable biological channelrhodopsin-2, photochromic spiropyran/anodic aluminium oxide nanochannel sensors are constructed to realize a light-controlled inert/active-switchable response to SO2 by ionic transport behaviour. We find that light can finely regulate the reactivit",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Membrane free-energy landscapes derived from atomistic dynamics explain nonuniversal cholesterol-induced stiffening",
    "doi": "10.1093/pnasnexus/pgad269",
    "url": "https://doi.org/10.1093/pnasnexus/pgad269",
    "journal": "npj Clean Energy",
    "year": 2023,
    "authors": "Fiorin, G.; Forrest, L.; Faraldo-Gómez, J.",
    "abstract": "Abstract\n                  All lipid membranes have inherent morphological preferences and resist deformation. Yet adaptations in membrane shape can and do occur at multiple length scales. While this plasticity is crucial for cellular physiology, the factors controlling the morphological energetics of lipid bilayers and the dominant mechanisms of membrane remodeling remain to be fully understood. An ongoing debate regarding the universality of the stiffening effect of cholesterol underscores the",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Bioinspired stability enhancement in deuterium-substituted organic–inorganic hybrid perovskite solar cells",
    "doi": "10.1093/pnasnexus/pgad160",
    "url": "https://doi.org/10.1093/pnasnexus/pgad160",
    "journal": "npj Clean Energy",
    "year": 2023,
    "authors": "Tong, J.; Li, X.; Wang, J.; He, H.; Xu, T.",
    "abstract": "Abstract\n               In hybrid perovskite solar cells (PSCs), the reaction of hydrogens (H) located in the amino group of the organic A-site cations with their neighboring halides plays a central role in degradation. Inspired by the retarded biological activities of cells in heavy water, we replaced the light H atom with its abundant, twice-as-heavy, nonradioactive isotope, deuterium (D) to hamper the motion of H. This D substitution retarded the formation kinetics of the detrimental H halide",
    "data_url": "",
    "source": "CrossRef",
    "direction": "CleanTech",
    "subcategory": "Solar PV & Storage",
    "direction_label": "Novel Low/Zero Carbon Technologies",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Prebiotic synthesis of mineral-bearing microdroplet from inorganic carbon photoreduction at air–water interface",
    "doi": "10.1093/pnasnexus/pgad389",
    "url": "https://doi.org/10.1093/pnasnexus/pgad389",
    "journal": "npj Clean Energy",
    "year": 2023,
    "authors": "Ge, Q.; Liu, Y.; You, W.; Wang, W.; Li, K.",
    "abstract": "Abstract\n               The origin of life on Earth is an enigmatic and intricate conundrum that has yet to be comprehensively resolved despite recent significant developments within the discipline of archaeology and geology. Chemically, metal-sulfide minerals are speculated to serve as an important medium for giving birth in early life, while yet so far direct evidence to support the hypothesis for the highly efficient conversion of inorganic carbon into praxiological biomolecules remains scarc",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Demand Response & IoT"
  },
  {
    "title": "Correction to: Structural measures of personal networks predict migrants’ cultural backgrounds: an explanation from Grid/Group theory",
    "doi": "10.1093/pnasnexus/pgad469",
    "url": "https://doi.org/10.1093/pnasnexus/pgad469",
    "journal": "npj Clean Energy",
    "year": 2023,
    "authors": "",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "New estimates of the storage permanence and ocean co-benefits of enhanced rock weathering",
    "doi": "10.1093/pnasnexus/pgad059",
    "url": "https://doi.org/10.1093/pnasnexus/pgad059",
    "journal": "npj Clean Energy",
    "year": 2023,
    "authors": "Kanzaki, Y.; Planavsky, N.; Reinhard, C.",
    "abstract": "Abstract\n               Avoiding many of the most severe consequences of anthropogenic climate change in the coming century will very likely require the development of “negative emissions technologies”—practices that lead to net carbon dioxide removal (CDR) from Earth's atmosphere. However, feedbacks within the carbon cycle place intrinsic limits on the long-term impact of CDR on atmospheric CO2 that are likely to vary across CDR technologies in ways that are poorly constrained. Here, we use an ",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "Novel Low/Zero Carbon Technologies",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Transformer fault diagnosis method based on TLR-ADASYN balanced dataset",
    "doi": "10.1038/s41598-023-49901-9",
    "url": "https://doi.org/10.1038/s41598-023-49901-9",
    "journal": "Scientific Reports",
    "year": 2023,
    "authors": "Guan, S.; Yang, H.; Wu, T.",
    "abstract": "AbstractAs the cornerstone of transmission and distribution equipment, power transformer plays a very important role in ensuring the safe operation of power system. At present, the technology of dissolved gas analysis (DGA) has been widely used in fault diagnosis of oil-immersed transformer. However, in the actual scene, the limited number of transformer fault samples and the uneven distribution of different fault types often lead to low overall fault detection accuracy or a few types of fault m",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "AI & Deep Learning"
  },
  {
    "title": "The role of double-skin facade configurations in optimizing building energy performance in Erbil city",
    "doi": "10.1038/s41598-023-35555-0",
    "url": "https://doi.org/10.1038/s41598-023-35555-0",
    "journal": "Scientific Reports",
    "year": 2023,
    "authors": "Naddaf, M.; Baper, S.",
    "abstract": "AbstractCarefully designing a building facade is the most crucial way to save energy, and a double-skin facade is an effective strategy for achieving energy efficiency. The improvement that can be made depends on how the double-skin facade is set up and what the weather conditions are like. This study was designed to investigate the best-case scenario with an appropriate double-skin facade configuration for optimizing building energy performance. A methodology for optimizing the building's initi",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "A big data association rule mining based approach for energy building behaviour analysis in an IoT environment",
    "doi": "10.1038/s41598-023-47056-1",
    "url": "https://doi.org/10.1038/s41598-023-47056-1",
    "journal": "Scientific Reports",
    "year": 2023,
    "authors": "Dolores, M.; Fernandez-Basso, C.; Gómez-Romero, J.; Martin-Bautista, M.",
    "abstract": "AbstractThe enormous amount of data generated by sensors and other data sources in modern grid management systems requires new infrastructures, such as IoT (Internet of Things) and Big Data architectures. This, in combination with Data Mining techniques, allows the management and processing of all these heterogeneous massive data in order to discover new insights that can help to reduce the energy consumption of the building. In this paper, we describe a developed methodology for an Internet of ",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Analysis of renewable energy consumption and economy considering the joint optimal allocation of “renewable energy + energy storage + synchronous condenser”",
    "doi": "10.1038/s41598-023-47401-4",
    "url": "https://doi.org/10.1038/s41598-023-47401-4",
    "journal": "Scientific Reports",
    "year": 2023,
    "authors": "Wang, Z.; Li, Q.; Kong, S.; Li, W.; Luo, J.",
    "abstract": "Abstract\n                  As renewable energy becomes increasingly dominant in the energy mix, the power system is evolving towards high proportions of renewable energy installations and power electronics-based equipment. This transition introduces significant challenges to the grid’s safe and stable operation. On the one hand, renewable energy generation equipment inherently provides weak voltage support, necessitating improvements in the voltage support capacity at renewable energy grid point",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Optimizing upside variability and antifragility in renewable energy system design",
    "doi": "10.1038/s41598-023-36379-8",
    "url": "https://doi.org/10.1038/s41598-023-36379-8",
    "journal": "Scientific Reports",
    "year": 2023,
    "authors": "Coppitters, D.; Contino, F.",
    "abstract": "AbstractDespite the considerable uncertainty in predicting critical parameters of renewable energy systems, the uncertainty during system design is often marginally addressed and consistently underestimated. Therefore, the resulting designs are fragile, with suboptimal performances when reality deviates significantly from the predicted scenarios. To address this limitation, we propose an antifragile design optimization framework that redefines the indicator to optimize variability and introduces",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Interrelationships between urban travel demand and electricity consumption: a deep learning approach",
    "doi": "10.1038/s41598-023-33133-y",
    "url": "https://doi.org/10.1038/s41598-023-33133-y",
    "journal": "Scientific Reports",
    "year": 2023,
    "authors": "Movahedi, A.; Parsa, A.; Rozhkov, A.; Lee, D.; Mohammadian, A.",
    "abstract": "AbstractThe analysis of infrastructure use data in relation to other components of the infrastructure can help better understand the interrelationships between infrastructures to eventually enhance their sustainability and resilience. In this study, we focus on electricity consumption and travel demand. In short, the premise is that when people are in buildings consuming electricity, they are not generating traffic on roads, and vice versa, hence the presence of interrelationships. We use Long S",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "AI & Deep Learning"
  },
  {
    "title": "Electricity consumption in Finland influenced by climate effects of energetic particle precipitation",
    "doi": "10.1038/s41598-023-47605-8",
    "url": "https://doi.org/10.1038/s41598-023-47605-8",
    "journal": "Scientific Reports",
    "year": 2023,
    "authors": "Juntunen, V.; Asikainen, T.",
    "abstract": "AbstractIt is known that electricity consumption in many cold Northern countries depends greatly on prevailing outdoor temperatures especially during the winter season. On the other hand, recent research has demonstrated that solar wind driven energetic particle precipitation from space into the polar atmosphere can influence the stratospheric polar vortex and tropospheric weather patterns during winter. These changes are significant, e.g., in Northern Europe, especially in Finland. In this stud",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "Novel Low/Zero Carbon Technologies",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Author Correction: Enhancing the Australian Gridded Climate Dataset rainfall analysis using satellite data",
    "doi": "10.1038/s41598-023-28997-z",
    "url": "https://doi.org/10.1038/s41598-023-28997-z",
    "journal": "Scientific Reports",
    "year": 2023,
    "authors": "Chua, Z.; Evans, A.; Kuleshov, Y.; Watkins, A.; Choy, S.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Optimized scheduling study of user side energy storage in cloud energy storage model",
    "doi": "10.1038/s41598-023-45673-4",
    "url": "https://doi.org/10.1038/s41598-023-45673-4",
    "journal": "Scientific Reports",
    "year": 2023,
    "authors": "Wang, H.; Yao, H.; Zhou, J.; Guo, Q.",
    "abstract": "AbstractWith the new round of power system reform, energy storage, as a part of power system frequency regulation and peaking, is an indispensable part of the reform. Among them, user-side small energy storage devices have the advantages of small size, flexible use and convenient application, but present decentralized characteristics in space. Therefore, the optimal allocation of small energy storage resources and the reduction of operating costs are urgent problems to be solved. In this study, ",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Simulation of melting paraffin with graphene nanoparticles within a solar thermal energy storage system",
    "doi": "10.1038/s41598-023-35361-8",
    "url": "https://doi.org/10.1038/s41598-023-35361-8",
    "journal": "Scientific Reports",
    "year": 2023,
    "authors": "Jafaryar, M.; Sheikholeslami, M.",
    "abstract": "AbstractIn this paper, applying new structure and loading Graphene nanoparticles have been considered as promising techniques for enhancing thermal storage systems. The layers within the paraffin zone were made from aluminum and the melting temperature of paraffin is 319.55 K. The paraffin zone located in the middle section of the triplex tube and uniform hot temperatures (335 K) for both walls of annulus have been applied. Three geometries for the container were applied with changing the angle ",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "Novel Low/Zero Carbon Technologies",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Forecasting the carbon footprint of civil buildings under different floor area growth trends and varying energy supply methods",
    "doi": "10.1038/s41598-023-49270-3",
    "url": "https://doi.org/10.1038/s41598-023-49270-3",
    "journal": "Scientific Reports",
    "year": 2023,
    "authors": "Teng, J.; Yin, H.",
    "abstract": "AbstractThe energy consumption and carbon footprint of buildings are significantly impacted by variations in building area and the number of households. Therefore, it is crucial to forecast the growth trend of building area and number of households. A validated time series model is used to predict the new building area in Jilin Province from 2023 to 2030. The new building area in Jilin Province is expected to exhibit two trends of growth in the future: rapid growth (S1) and slow growth (S2). By ",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "Carbon Trading & New Business Models",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Blending controlled-release urea and urea under ridge-furrow with plastic film mulching improves yield while mitigating carbon footprint in rainfed potato",
    "doi": "10.1038/s41598-022-25845-4",
    "url": "https://doi.org/10.1038/s41598-022-25845-4",
    "journal": "Scientific Reports",
    "year": 2023,
    "authors": "Sun, M.; Ma, B.; Lu, P.; Bai, J.; Mi, J.",
    "abstract": "AbstractRidge-furrow with plastic film mulching and various urea types have been applied in rainfed agriculture, but their interactive effects on potato (Solanum tuberosum L.) yield and especially environments remain poorly understood. A three-year experiment was conducted to explore the responses of tuber yield, methane (CH4) and nitrous oxide (N2O) emissions, net global warming potential (NGWP), carbon footprint (CF), and net ecosystem economic budget (NEEB) of rainfed potato to two mulching p",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "Carbon Trading & New Business Models",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Comprehensive energy efficiency optimization algorithm for steel load considering network reconstruction and demand response",
    "doi": "10.1038/s41598-023-46804-7",
    "url": "https://doi.org/10.1038/s41598-023-46804-7",
    "journal": "Scientific Reports",
    "year": 2023,
    "authors": "Zang, Y.; Wang, S.; Ge, W.; Li, Y.; Cui, J.",
    "abstract": "AbstractIndustrial loads are usually energy intensive and inefficient. The optimization of energy efficiency management in steel plants is still in the early stage of development. Considering the topology of power grid, it is an urgent problem to improve the operation economy and load side energy efficiency of steel plants. In this paper, a two-level collaborative optimization method is proposed, which takes into account the dynamic reconstruction cost, transmission loss cost, energy cost and de",
    "data_url": "",
    "source": "CrossRef",
    "direction": "FLEXERGY",
    "subcategory": "Demand Response",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Impact of implementing emergency demand response program and tie-line on cyber-physical distribution network resiliency",
    "doi": "10.1038/s41598-023-30746-1",
    "url": "https://doi.org/10.1038/s41598-023-30746-1",
    "journal": "Scientific Reports",
    "year": 2023,
    "authors": "Osman, S.; Sedhom, B.; Kaddah, S.",
    "abstract": "AbstractRecently, due to the complex nature of cyber-physical distribution networks (DNs) and the severity of power outages caused by natural disasters, microgrid (MG) formation, distributed renewable energy resources (DRERs), and demand response programs (DRP) have been employed to enhance the resiliency of these networks. This paper proposes a novel multi-objective MGs formation method-based darts game theory optimization algorithm. The microgrid formation is obtained by controlling the sectio",
    "data_url": "",
    "source": "CrossRef",
    "direction": "FLEXERGY",
    "subcategory": "Demand Response",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "The value of fusion energy to a decarbonized United States electric grid",
    "doi": "10.1016/j.joule.2023.02.006",
    "url": "https://doi.org/10.1016/j.joule.2023.02.006",
    "journal": "Joule",
    "year": 2023,
    "authors": "Schwartz, J.; Ricks, W.; Kolemen, E.; Jenkins, J.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Coordinating distributed energy resources for reliability can significantly reduce future distribution grid upgrades and peak load",
    "doi": "10.1016/j.joule.2023.06.015",
    "url": "https://doi.org/10.1016/j.joule.2023.06.015",
    "journal": "Joule",
    "year": 2023,
    "authors": "Navidi, T.; El Gamal, A.; Rajagopal, R.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Two million European single-family homes could abandon the grid by 2050",
    "doi": "10.1016/j.joule.2023.09.012",
    "url": "https://doi.org/10.1016/j.joule.2023.09.012",
    "journal": "Joule",
    "year": 2023,
    "authors": "Kleinebrahm, M.; Weinand, J.; Naber, E.; McKenna, R.; Ardone, A.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Thermally activated batteries and their prospects for grid-scale energy storage",
    "doi": "10.1016/j.joule.2023.02.009",
    "url": "https://doi.org/10.1016/j.joule.2023.02.009",
    "journal": "Joule",
    "year": 2023,
    "authors": "Li, M.; Weller, J.; Reed, D.; Sprenkle, V.; Li, G.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Statistical and machine learning-based durability-testing strategies for energy storage",
    "doi": "10.1016/j.joule.2023.03.008",
    "url": "https://doi.org/10.1016/j.joule.2023.03.008",
    "journal": "Joule",
    "year": 2023,
    "authors": "Harris, S.; Noack, M.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "AI & Deep Learning"
  },
  {
    "title": "Reviewing the sociotechnical dynamics of carbon removal",
    "doi": "10.1016/j.joule.2022.11.008",
    "url": "https://doi.org/10.1016/j.joule.2022.11.008",
    "journal": "Joule",
    "year": 2023,
    "authors": "Sovacool, B.; Baum, C.; Low, S.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Demand Response & IoT"
  },
  {
    "title": "Getting methane under control: Paper policies, practical measurements, and the urgent need to verify emissions",
    "doi": "10.1016/j.oneear.2023.04.013",
    "url": "https://doi.org/10.1016/j.oneear.2023.04.013",
    "journal": "One Earth",
    "year": 2023,
    "authors": "Nisbet, E.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Optimization of solar and battery-based hybrid renewable energy system augmented with bioenergy and hydro energy-based dispatchable source",
    "doi": "10.1016/j.isci.2022.105821",
    "url": "https://doi.org/10.1016/j.isci.2022.105821",
    "journal": "iScience",
    "year": 2023,
    "authors": "Memon, S.; Upadhyay, D.; Patel, R.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "Novel Low/Zero Carbon Technologies",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Hierarchical approach to evaluating storage requirements for renewable-energy-driven grids",
    "doi": "10.1016/j.isci.2022.105900",
    "url": "https://doi.org/10.1016/j.isci.2022.105900",
    "journal": "iScience",
    "year": 2023,
    "authors": "Mahmud, Z.; Shiraishi, K.; Abido, M.; Sánchez-Pérez, P.; Kurtz, S.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Region-wise evaluation of price-based demand response programs in Japan’s wholesale electricity market considering microeconomic equilibrium",
    "doi": "10.1016/j.isci.2023.106978",
    "url": "https://doi.org/10.1016/j.isci.2023.106978",
    "journal": "iScience",
    "year": 2023,
    "authors": "Malehmirchegini, L.; Suliman, M.; Farzaneh, H.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "FLEXERGY",
    "subcategory": "Demand Response",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "Demand Response & IoT"
  },
  {
    "title": "Death spiral of the legacy grid: A game-theoretic analysis of modern grid defection processes",
    "doi": "10.1016/j.isci.2023.106415",
    "url": "https://doi.org/10.1016/j.isci.2023.106415",
    "journal": "iScience",
    "year": 2023,
    "authors": "Navon, A.; Belikov, J.; Ofir, R.; Parag, Y.; Orda, A.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Intrinsic theta oscillation in the attractor network of grid cells",
    "doi": "10.1016/j.isci.2023.106351",
    "url": "https://doi.org/10.1016/j.isci.2023.106351",
    "journal": "iScience",
    "year": 2023,
    "authors": "Wang, Z.; Wang, T.; Yang, F.; Liu, F.; Wang, W.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Coherently remapping toroidal cells but not Grid cells are responsible for path integration in virtual agents",
    "doi": "10.1016/j.isci.2023.108102",
    "url": "https://doi.org/10.1016/j.isci.2023.108102",
    "journal": "iScience",
    "year": 2023,
    "authors": "Schøyen, V.; Pettersen, M.; Holzhausen, K.; Fyhn, M.; Malthe-Sørenssen, A.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Data-driven multi-objective optimization for electric vehicle charging infrastructure",
    "doi": "10.1016/j.isci.2023.107737",
    "url": "https://doi.org/10.1016/j.isci.2023.107737",
    "journal": "iScience",
    "year": 2023,
    "authors": "Farhadi, F.; Wang, S.; Palacin, R.; Blythe, P.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "FLEXERGY",
    "subcategory": "Electric Vehicles & Mobility",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Mind the goal: Trade-offs between flexibility goals for controlled electric vehicle charging strategies",
    "doi": "10.1016/j.isci.2023.105937",
    "url": "https://doi.org/10.1016/j.isci.2023.105937",
    "journal": "iScience",
    "year": 2023,
    "authors": "Gschwendtner, C.; Knoeri, C.; Stephan, A.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "FLEXERGY",
    "subcategory": "Electric Vehicles & Mobility",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "An overview of deterministic and probabilistic forecasting methods of wind energy",
    "doi": "10.1016/j.isci.2022.105804",
    "url": "https://doi.org/10.1016/j.isci.2022.105804",
    "journal": "iScience",
    "year": 2023,
    "authors": "Xie, Y.; Li, C.; Li, M.; Liu, F.; Taukenova, M.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Spatiotemporal analysis of the future carbon footprint of solar electricity in the United States by a dynamic life cycle assessment",
    "doi": "10.1016/j.isci.2023.106188",
    "url": "https://doi.org/10.1016/j.isci.2023.106188",
    "journal": "iScience",
    "year": 2023,
    "authors": "Lu, J.; Tang, J.; Shan, R.; Li, G.; Rao, P.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "Novel Low/Zero Carbon Technologies",
    "refined_category": "Demand Response & IoT"
  },
  {
    "title": "Nonlinear terahertz control of the lead halide perovskite lattice",
    "doi": "10.1126/sciadv.adg3856",
    "url": "https://doi.org/10.1126/sciadv.adg3856",
    "journal": "Science Advances",
    "year": 2023,
    "authors": "Frenzel, M.; Cherasse, M.; Urban, J.; Wang, F.; Xiang, B.",
    "abstract": "\n            Lead halide perovskites (LHPs) have emerged as an excellent class of semiconductors for next-generation solar cells and optoelectronic devices. Tailoring physical properties by fine-tuning the lattice structures has been explored in these materials by chemical composition or morphology. Nevertheless, its dynamic counterpart, phonon-driven ultrafast material control, as contemporarily harnessed for oxide perovskites, has not yet been established. Here, we use intense THz electric fie",
    "data_url": "",
    "source": "CrossRef",
    "direction": "CleanTech",
    "subcategory": "Solar PV & Storage",
    "direction_label": "Novel Low/Zero Carbon Technologies",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Manipulating nitration and stabilization to achieve high energy",
    "doi": "10.1126/sciadv.adk3754",
    "url": "https://doi.org/10.1126/sciadv.adk3754",
    "journal": "Science Advances",
    "year": 2023,
    "authors": "Singh, J.; Staples, R.; Shreeve, J.",
    "abstract": "\n            Nitro groups have played a central and decisive role in the development of the most powerful known energetic materials. Highly nitrated compounds are potential oxidizing agents, which could replace the environmentally hazardous used materials such as ammonium perchlorate. The scarcity of azole compounds with a large number of nitro groups is likely due to their inherent thermal instability and the limited number of ring sites available for bond formation. Now, the formation of the f",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Machine learning for industrial processes: Forecasting amine emissions from a carbon capture plant",
    "doi": "10.1126/sciadv.adc9576",
    "url": "https://doi.org/10.1126/sciadv.adc9576",
    "journal": "Science Advances",
    "year": 2023,
    "authors": "Jablonka, K.; Charalambous, C.; Sanchez Fernandez, E.; Wiechers, G.; Monteiro, J.",
    "abstract": "One of the main environmental impacts of amine-based carbon capture processes is the emission of the solvent into the atmosphere. To understand how these emissions are affected by the intermittent operation of a power plant, we performed stress tests on a plant operating with a mixture of two amines, 2-amino-2-methyl-1-propanol and piperazine (CESAR1). To forecast the emissions and model the impact of interventions, we developed a machine learning model. Our model showed that some interventions ",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "Novel Low/Zero Carbon Technologies",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Injectable, self-healing hydrogel adhesives with firm tissue adhesion and on-demand biodegradation for sutureless wound closure",
    "doi": "10.1126/sciadv.adh4327",
    "url": "https://doi.org/10.1126/sciadv.adh4327",
    "journal": "Science Advances",
    "year": 2023,
    "authors": "Ren, H.; Zhang, Z.; Cheng, X.; Zou, Z.; Chen, X.",
    "abstract": "\n            Tissue adhesives have garnered extensive interest as alternatives and supplements to sutures, whereas major challenges still remain, including weak tissue adhesion, inadequate biocompatibility, and uncontrolled biodegradation. Here, injectable and biocompatible hydrogel adhesives are developed via catalyst-free\n            o-\n            phthalaldehyde/amine (hydrazide) cross-linking reaction. The hydrogels demonstrate rapid and firm adhesion to various tissues, and an\n            o",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Swarming self-adhesive microgels enabled aneurysm on-demand embolization in physiological blood flow",
    "doi": "10.1126/sciadv.adf9278",
    "url": "https://doi.org/10.1126/sciadv.adf9278",
    "journal": "Science Advances",
    "year": 2023,
    "authors": "Jin, D.; Wang, Q.; Chan, K.; Xia, N.; Yang, H.",
    "abstract": "The recent rise of swarming microrobotics offers great promise in the revolution of minimally invasive embolization procedure for treating aneurysm. However, targeted embolization treatment of aneurysm using microrobots has significant challenges in the delivery capability and filling controllability. Here, we develop an interventional catheterization-integrated swarming microrobotic platform for aneurysm on-demand embolization in physiological blood flow. A pH-responsive self-healing hydrogel d",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Grid-based methods for chemistry simulations on a quantum computer",
    "doi": "10.1126/sciadv.abo7484",
    "url": "https://doi.org/10.1126/sciadv.abo7484",
    "journal": "Science Advances",
    "year": 2023,
    "authors": "Chan, H.; Meister, R.; Jones, T.; Tew, D.; Benjamin, S.",
    "abstract": "First-quantized, grid-based methods for chemistry modeling are a natural and elegant fit for quantum computers. However, it is infeasible to use today’s quantum prototypes to explore the power of this approach because it requires a substantial number of near-perfect qubits. Here, we use exactly emulated quantum computers with up to 36 qubits to execute deep yet resource-frugal algorithms that model 2D and 3D atoms with single and paired particles. A range of tasks is explored, from ground state ",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Light-stimulated micromotor swarms in an electric field with accurate spatial, temporal, and mode control",
    "doi": "10.1126/sciadv.adi9932",
    "url": "https://doi.org/10.1126/sciadv.adi9932",
    "journal": "Science Advances",
    "year": 2023,
    "authors": "Liang, Z.; Joh, H.; Lian, B.; Fan, D.",
    "abstract": "Swarming, a phenomenon widely present in nature, is a hallmark of nonequilibrium living systems that harness external energy into collective locomotion. The creation and study of manmade swarms may provide insights into their biological counterparts and shed light to the rules of life. Here, we propose an innovative mechanism for rationally creating multimodal swarms with unprecedented spatial, temporal, and mode control. The research is realized in a system made of optoelectric semiconductor na",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Toward highly effective loading of DNA in hydrogels for high-density and long-term information storage",
    "doi": "10.1126/sciadv.adg9933",
    "url": "https://doi.org/10.1126/sciadv.adg9933",
    "journal": "Science Advances",
    "year": 2023,
    "authors": "Fei, Z.; Gupta, N.; Li, M.; Xiao, P.; Hu, X.",
    "abstract": "\n            Digital information, when converted into a DNA sequence, provides dense, stable, energy-efficient, and sustainable data storage. The most stable method for encapsulating DNA has been in an inorganic matrix of silica, iron oxide, or both, but are limited by low DNA uptake and complex recovery techniques. This study investigated a rationally designed thermally responsive functionally graded (TRFG) hydrogel as a simple and cost-effective method for storing DNA. The TRFG hydrogel shows ",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "An all-Africa dataset of energy model “supply regions” for solar photovoltaic and wind power",
    "doi": "10.1038/s41597-022-01786-5",
    "url": "https://doi.org/10.1038/s41597-022-01786-5",
    "journal": "Scientific Data",
    "year": 2022,
    "authors": "Sterl, S.; Hussain, B.; Miketa, A.; Li, Y.; Merven, B.",
    "abstract": "AbstractWith solar and wind power generation reaching unprecedented growth rates globally, much research effort has recently gone into a comprehensive mapping of the worldwide potential of these variable renewable electricity (VRE) sources. From a perspective of energy systems analysis, the locations with the strongest resources may not necessarily be the best candidates for investment in new power plants, since the distance from existing grid and road infrastructures and the temporal variabilit",
    "data_url": "",
    "source": "CrossRef",
    "direction": "CleanTech",
    "subcategory": "Solar PV & Storage",
    "direction_label": "Novel Low/Zero Carbon Technologies",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "A long-term reconstructed TROPOMI solar-induced fluorescence dataset using machine learning algorithms",
    "doi": "10.1038/s41597-022-01520-1",
    "url": "https://doi.org/10.1038/s41597-022-01520-1",
    "journal": "Scientific Data",
    "year": 2022,
    "authors": "Chen, X.; Huang, Y.; Nie, C.; Zhang, S.; Wang, G.",
    "abstract": "AbstractPhotosynthesis is a key process linking carbon and water cycles, and satellite-retrieved solar-induced chlorophyll fluorescence (SIF) can be a valuable proxy for photosynthesis. The TROPOspheric Monitoring Instrument (TROPOMI) on the Copernicus Sentinel-5P mission enables significant improvements in providing high spatial and temporal resolution SIF observations, but the short temporal coverage of the data records has limited its applications in long-term studies. This study uses machine",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "Novel Low/Zero Carbon Technologies",
    "refined_category": "AI & Deep Learning"
  },
  {
    "title": "A high spatial resolution dataset for anthropogenic atmospheric mercury emissions in China during 1998–2014",
    "doi": "10.1038/s41597-022-01725-4",
    "url": "https://doi.org/10.1038/s41597-022-01725-4",
    "journal": "Scientific Data",
    "year": 2022,
    "authors": "Chang, W.; Zhong, Q.; Liang, S.; Qi, J.; Jetashree, .",
    "abstract": "AbstractChina is the largest atmospheric mercury (Hg) emitter globally, which has been substantially investigated. However, the estimation of national or regional Hg emissions in China is insufficient in supporting emission control, as the location of the sources may have significant impacts on the effects of Hg emissions. In this concern, high-spatial-resolution datasets of China’s Hg emissions are necessary for in-depth and accurate Hg-related studies and policymaking. Existing gridded dataset",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "Carbon Trading & New Business Models",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Solar and wind power data from the Chinese State Grid Renewable Energy Generation Forecasting Competition",
    "doi": "10.1038/s41597-022-01696-6",
    "url": "https://doi.org/10.1038/s41597-022-01696-6",
    "journal": "Scientific Data",
    "year": 2022,
    "authors": "Chen, Y.; Xu, J.",
    "abstract": "AbstractAccurate solar and wind generation forecasting along with high renewable energy penetration in power grids throughout the world are crucial to the days-ahead power scheduling of energy systems. It is difficult to precisely forecast on-site power generation due to the intermittency and fluctuation characteristics of solar and wind energy. Solar and wind generation data from on-site sources are beneficial for the development of data-driven forecasting models. In this paper, an open dataset",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "Novel Low/Zero Carbon Technologies",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Datasets on South Korean manufacturing factories’ electricity consumption and demand response participation",
    "doi": "10.1038/s41597-022-01357-8",
    "url": "https://doi.org/10.1038/s41597-022-01357-8",
    "journal": "Scientific Data",
    "year": 2022,
    "authors": "Lee, E.; Baek, K.; Kim, J.",
    "abstract": "AbstractThis study describes the release of electricity consumption data of some manufacturing factories located in South Korea that participate in the demand response (DR) market. The data (in kilowatt) comprise individual factories’ total power usage details that were acquired using advanced metering infrastructures. They further contain details on the manufacture types, DR participation dates, mandatory reduction capacities, and response capacities of the factories. For data acquisition, 10 m",
    "data_url": "",
    "source": "CrossRef",
    "direction": "FLEXERGY",
    "subcategory": "Demand Response",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "Demand Response & IoT"
  },
  {
    "title": "A residential labeled dataset for smart meter data analytics",
    "doi": "10.1038/s41597-022-01252-2",
    "url": "https://doi.org/10.1038/s41597-022-01252-2",
    "journal": "Scientific Data",
    "year": 2022,
    "authors": "Pereira, L.; Costa, D.; Ribeiro, M.",
    "abstract": "AbstractSmart meter data is a cornerstone for the realization of next-generation electrical power grids by enabling the creation of novel energy data-based services like providing recommendations on how to save energy or predictive maintenance of electric appliances. Most of these services are developed on top of advanced machine-learning algorithms, which rely heavily on datasets for training, testing, and validation purposes. A limitation of most existing datasets, however, is the scarcity of ",
    "data_url": "",
    "source": "CrossRef",
    "direction": "FLEXERGY",
    "subcategory": "Smart Home & EMS",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Planning sustainable electricity solutions for refugee settlements in sub-Saharan Africa",
    "doi": "10.1038/s41560-022-01006-9",
    "url": "https://doi.org/10.1038/s41560-022-01006-9",
    "journal": "Nature Energy",
    "year": 2022,
    "authors": "Baldi, D.; Moner-Girona, M.; Fumagalli, E.; Fahl, F.",
    "abstract": "AbstractAn inadequate understanding of the energy needs of forcibly displaced populations is one of the main obstacles in providing sustainable and reliable energy to refugees and their host communities. Here, we provide a first-order assessment of the main factors determining the decision to deploy fully renewable mini-grids in almost 300 refugee settlements in sub-Saharan Africa. Using an energy assessment survey and publicly available traditional and earth observation data, we estimate a tota",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Charging infrastructure access and operation to reduce the grid impacts of deep electric vehicle adoption",
    "doi": "10.1038/s41560-022-01105-7",
    "url": "https://doi.org/10.1038/s41560-022-01105-7",
    "journal": "Nature Energy",
    "year": 2022,
    "authors": "Powell, S.; Cezar, G.; Min, L.; Azevedo, I.; Rajagopal, R.",
    "abstract": "AbstractElectric vehicles will contribute to emissions reductions in the United States, but their charging may challenge electricity grid operations. We present a data-driven, realistic model of charging demand that captures the diverse charging behaviours of future adopters in the US Western Interconnection. We study charging control and infrastructure build-out as critical factors shaping charging load and evaluate grid impact under rapid electric vehicle adoption with a detailed economic disp",
    "data_url": "",
    "source": "CrossRef",
    "direction": "FLEXERGY",
    "subcategory": "Electric Vehicles & Mobility",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Towards a repair research agenda for off-grid solar e-waste in the Global South",
    "doi": "10.1038/s41560-022-01103-9",
    "url": "https://doi.org/10.1038/s41560-022-01103-9",
    "journal": "Nature Energy",
    "year": 2022,
    "authors": "Munro, P.; Samarakoon, S.; Hansen, U.; Kearnes, M.; Bruce, A.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "Novel Low/Zero Carbon Technologies",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Simulated co-optimization of renewable energy and desalination systems in Neom, Saudi Arabia",
    "doi": "10.1038/s41467-022-31233-3",
    "url": "https://doi.org/10.1038/s41467-022-31233-3",
    "journal": "Nature Communications",
    "year": 2022,
    "authors": "Riera, J.; Lima, R.; Hoteit, I.; Knio, O.",
    "abstract": "AbstractThe interdependence between the water and power sectors is a growing concern as the need for desalination increases globally. Therefore, co-optimizing interdependent systems is necessary to understand the impact of one sector on another. We propose a framework to identify the optimal investment mix for a co-optimized water-power system and apply it to Neom, Saudi Arabia. Our results show that investment strategies that consider the co-optimization of both systems result in total cost sav",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "Novel Low/Zero Carbon Technologies",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Synchronization in electric power networks with inherent heterogeneity up to 100% inverter-based renewable generation",
    "doi": "10.1038/s41467-022-30164-3",
    "url": "https://doi.org/10.1038/s41467-022-30164-3",
    "journal": "Nature Communications",
    "year": 2022,
    "authors": "Sajadi, A.; Kenyon, R.; Hodge, B.",
    "abstract": "AbstractThe synchronized operation of power generators is the foundation of electric power network stability and a key to the prevention of undesired power outages and blackouts. Here, we derive the conditions that guarantee synchronization in power networks with inherent generator heterogeneity when subjected to small perturbations, and perform a parametric sensitivity analysis to understand synchronization with varied types of generators. As inverter-based resources, which are the primary inte",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Data-driven load profiles and the dynamics of residential electricity consumption",
    "doi": "10.1038/s41467-022-31942-9",
    "url": "https://doi.org/10.1038/s41467-022-31942-9",
    "journal": "Nature Communications",
    "year": 2022,
    "authors": "Anvari, M.; Proedrou, E.; Schäfer, B.; Beck, C.; Kantz, H.",
    "abstract": "AbstractThe dynamics of power consumption constitutes an essential building block for planning and operating sustainable energy systems. Whereas variations in the dynamics of renewable energy generation are reasonably well studied, a deeper understanding of the variations in consumption dynamics is still missing. Here, we analyse highly resolved residential electricity consumption data of Austrian, German and UK households and propose a generally applicable data-driven load model. Specifically, ",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Electrifying passenger road transport in India requires near-term electricity grid decarbonisation",
    "doi": "10.1038/s41467-022-29620-x",
    "url": "https://doi.org/10.1038/s41467-022-29620-x",
    "journal": "Nature Communications",
    "year": 2022,
    "authors": "Abdul-Manan, A.; Gordillo Zavaleta, V.; Agarwal, A.; Kalghatgi, G.; Amer, A.",
    "abstract": "AbstractBattery-electric vehicles (BEV) have emerged as a favoured technology solution to mitigate transport greenhouse gas (GHG) emissions in many non-Annex 1 countries, including India. GHG mitigation potentials of electric 4-wheelers in India depend critically on when and where they are charged: 40% reduction in the north-eastern states and more than 15% increase in the eastern/western regions today, with higher overall GHGs emitted when charged overnight and in the summer. Self-charging gaso",
    "data_url": "",
    "source": "CrossRef",
    "direction": "FLEXERGY",
    "subcategory": "Electric Vehicles & Mobility",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Disruption of the grid cell network in a mouse model of early Alzheimer’s disease",
    "doi": "10.1038/s41467-022-28551-x",
    "url": "https://doi.org/10.1038/s41467-022-28551-x",
    "journal": "Nature Communications",
    "year": 2022,
    "authors": "Ying, J.; Keinath, A.; Lavoie, R.; Vigneault, E.; El Mestikawy, S.",
    "abstract": "Abstract\n                  Early-onset familial Alzheimer’s disease (AD) is marked by an aggressive buildup of amyloid beta (Aβ) proteins, yet the neural circuit operations impacted during the initial stages of Aβ pathogenesis remain elusive. Here, we report a coding impairment of the medial entorhinal cortex (MEC) grid cell network in the J20 transgenic mouse model of familial AD that over-expresses Aβ throughout the hippocampus and entorhinal cortex. Grid cells showed reduced spatial periodici",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Electro-active metaobjective from metalenses-on-demand",
    "doi": "10.1038/s41467-022-34494-0",
    "url": "https://doi.org/10.1038/s41467-022-34494-0",
    "journal": "Nature Communications",
    "year": 2022,
    "authors": "Karst, J.; Lee, Y.; Floess, M.; Ubl, M.; Ludwigs, S.",
    "abstract": "AbstractSwitchable metasurfaces can actively control the functionality of integrated metadevices with high efficiency and on ultra-small length scales. Such metadevices include active lenses, dynamic diffractive optical elements, or switchable holograms. Especially, for applications in emerging technologies such as AR (augmented reality) and VR (virtual reality) devices, sophisticated metaoptics with unique functionalities are crucially important. In particular, metaoptics which can be switched ",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Magnetically assisted drop-on-demand 3D printing of microstructured multimaterial composites",
    "doi": "10.1038/s41467-022-32792-1",
    "url": "https://doi.org/10.1038/s41467-022-32792-1",
    "journal": "Nature Communications",
    "year": 2022,
    "authors": "Liu, W.; Chou, V.; Behera, R.; Le Ferrand, H.",
    "abstract": "AbstractMicrostructured composites with hierarchically arranged fillers fabricated by three-dimensional (3D) printing show enhanced properties along the fillers’ alignment direction. However, it is still challenging to achieve good control of the filler arrangement and high filler concentration simultaneously, which limits the printed material’s properties. In this study, we develop a magnetically assisted drop-on-demand 3D printing technique (MDOD) to print aligned microplatelet reinforced comp",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Feasibility of hybrid in-stream generator–photovoltaic systems for Amazonian off-grid communities",
    "doi": "10.1093/pnasnexus/pgac077",
    "url": "https://doi.org/10.1093/pnasnexus/pgac077",
    "journal": "npj Clean Energy",
    "year": 2022,
    "authors": "Brown, E.; Johansen, I.; Bortoleto, A.; Pokhrel, Y.; Chaudhari, S.",
    "abstract": "Abstract\n               While there have been efforts to supply off-grid energy in the Amazon, these attempts have focused on low upfront costs and deployment rates. These “get-energy-quick” methods have almost solely adopted diesel generators, ignoring the environmental and social risks associated with the known noise and pollution of combustion engines. Alternatively, it is recommended, herein, to supply off-grid needs with renewable, distributed microgrids comprised of photovoltaics (PV) and ",
    "data_url": "",
    "source": "CrossRef",
    "direction": "CleanTech",
    "subcategory": "Solar PV & Storage",
    "direction_label": "Novel Low/Zero Carbon Technologies",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Unexpected no significant soil carbon losses in the Tibetan grasslands due to rodent bioturbation",
    "doi": "10.1093/pnasnexus/pgac314",
    "url": "https://doi.org/10.1093/pnasnexus/pgac314",
    "journal": "npj Clean Energy",
    "year": 2022,
    "authors": "Huang, M.; Gan, D.; Li, Z.; Wang, J.; Niu, S.",
    "abstract": "AbstractThe Tibetan grasslands store 2.5% of the Earth’s soil organic carbon. Unsound management practices and climate change have resulted in widespread grassland degradation, providing open habitats for rodent activities. Rodent bioturbation loosens topsoil, reduces productivity, changes soil nutrient conditions, and consequently influences the soil organic carbon stocks of the Tibetan grasslands. However, these effects have not been quantified. Here, using meta-analysis and upscaling approach",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Demand Response & IoT"
  },
  {
    "title": "Structural measures of personal networks predict migrants’ cultural backgrounds: an explanation from Grid/Group theory",
    "doi": "10.1093/pnasnexus/pgac195",
    "url": "https://doi.org/10.1093/pnasnexus/pgac195",
    "journal": "npj Clean Energy",
    "year": 2022,
    "authors": "Molina, J.; Ozaita, J.; Tamarit, I.; Sánchez, A.; McCarty, C.",
    "abstract": "Abstract\n               Culture and social structure are not separated analytical domains but intertwined phenomena observable in personal networks. Drawing on a personal networks dataset of migrants in the United States and Spain, we show that the country of origin, a proxy for diverse languages and cultural institutions, and religion may be predicted by specific combinations of personal network structural measures (closeness, clustering, betweenness, average degree, etc). We obtain similar res",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Cryocampsis: a biophysical freeze-bending response of shrubs and trees under snow loads",
    "doi": "10.1093/pnasnexus/pgac131",
    "url": "https://doi.org/10.1093/pnasnexus/pgac131",
    "journal": "npj Clean Energy",
    "year": 2022,
    "authors": "Ray, P.; Bret-Harte, M.",
    "abstract": "Abstract\n               We report a biophysical mechanism, termed cryocampsis (Greek cryo-, cold, + campsis, bending), that helps northern shrubs bend downward under a snow load. Subfreezing temperatures substantially increase the downward bending of cantilever-loaded branches of these shrubs, while allowing them to recover their summer elevation after thawing and becoming unloaded. This is counterintuitive, because biological materials (including branches that show cryocampsis) generally become",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Energy and thermal modelling of an office building to develop an artificial neural networks model",
    "doi": "10.1038/s41598-022-12924-9",
    "url": "https://doi.org/10.1038/s41598-022-12924-9",
    "journal": "Scientific Reports",
    "year": 2022,
    "authors": "Santos-Herrero, J.; Lopez-Guede, J.; Flores Abascal, I.; Zulueta, E.",
    "abstract": "AbstractNowadays everyone should be aware of the importance of reducing CO2 emissions which produce the greenhouse effect. In the field of construction, several options are proposed to reach nearly-Zero Energy Building (nZEB) standards. Obviously, before undertaking a modification in any part of a building focused on improving the energy performance, it is generally better to carry out simulations to evaluate its effectiveness. Using Artificial Neural Networks (ANNs) allows a digital twin of the",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "AI & Deep Learning"
  },
  {
    "title": "Enhancing the Australian Gridded Climate Dataset rainfall analysis using satellite data",
    "doi": "10.1038/s41598-022-25255-6",
    "url": "https://doi.org/10.1038/s41598-022-25255-6",
    "journal": "Scientific Reports",
    "year": 2022,
    "authors": "Chua, Z.; Evans, A.; Kuleshov, Y.; Watkins, A.; Choy, S.",
    "abstract": "AbstractRainfall estimation over large areas is important for a thorough understanding of water availability, influencing societal decision-making, as well as being an input for scientific models. Traditionally, Australia utilizes a gauge-based analysis for rainfall estimation, but its performance can be severely limited over regions with low gauge density such as central parts of the continent. At the Australian Bureau of Meteorology, the current operational monthly rainfall component of the Au",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Linking the long-term variability in global wave energy to swell climate and redefining suitable coasts for energy exploitation",
    "doi": "10.1038/s41598-022-18935-w",
    "url": "https://doi.org/10.1038/s41598-022-18935-w",
    "journal": "Scientific Reports",
    "year": 2022,
    "authors": "Kamranzad, B.; Amarouche, K.; Akpinar, A.",
    "abstract": "AbstractThe sustainability of wave energy linked to the intra- and inter-annual variability in wave climate is crucial in wave resource assessment. In this study, we quantify the dependency of stability of wave energy flux (power) on long-term variability of wind and wave climate to detect a relationship between them. We used six decades of re-analysis wind and simulated wave climate in the entire globe and using two 30-yearly periods, we showed that not only the previously suggested minimum per",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Authentication of smart grid communications using quantum key distribution",
    "doi": "10.1038/s41598-022-16090-w",
    "url": "https://doi.org/10.1038/s41598-022-16090-w",
    "journal": "Scientific Reports",
    "year": 2022,
    "authors": "Alshowkan, M.; Evans, P.; Starke, M.; Earl, D.; Peters, N.",
    "abstract": "AbstractSmart grid solutions enable utilities and customers to better monitor and control energy use via information and communications technology. Information technology is intended to improve the future electric grid’s reliability, efficiency, and sustainability by implementing advanced monitoring and control systems. However, leveraging modern communications systems also makes the grid vulnerable to cyberattacks. Here we report the first use of quantum key distribution (QKD) keys in the authe",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Ecological driving on multiphase trajectories and multiobjective optimization for autonomous electric vehicle platoon",
    "doi": "10.1038/s41598-022-09156-2",
    "url": "https://doi.org/10.1038/s41598-022-09156-2",
    "journal": "Scientific Reports",
    "year": 2022,
    "authors": "Xiaofeng, T.",
    "abstract": "AbstractAutonomous electric vehicles promise to improve traffic safety, increase fuel efficiency and reduce congestion in future intelligent transportation systems. Ecological driving characteristics are first studied to concentrate on energy consumption, the ability to quickly pass its destination, etc. of autonomous electric vehicle plans (AEVPs) to maximize total energy efficiency benefits. To realize this goal, an optimal control model is developed to provide ecological driving suggestions t",
    "data_url": "",
    "source": "CrossRef",
    "direction": "FLEXERGY",
    "subcategory": "Electric Vehicles & Mobility",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Collection mode choice of spent electric vehicle batteries: considering collection competition and third-party economies of scale",
    "doi": "10.1038/s41598-022-10433-3",
    "url": "https://doi.org/10.1038/s41598-022-10433-3",
    "journal": "Scientific Reports",
    "year": 2022,
    "authors": "Li, X.",
    "abstract": "AbstractWith the rapid development of the electric vehicle (EV) industry, the recycling of spent EV batteries has attracted considerable attention. The establishment and optimization of the collection mode is a key link in regulating the recycling of spent EV batteries. This paper investigates an EV battery supply chain including an EV manufacturer, an EV retailer, and a third-party collector and analyzes three dual-channel collection modes. The optimal pricing and collection decisions of the th",
    "data_url": "",
    "source": "CrossRef",
    "direction": "FLEXERGY",
    "subcategory": "Electric Vehicles & Mobility",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Enhancing wind direction prediction of South Africa wind energy hotspots with Bayesian mixture modeling",
    "doi": "10.1038/s41598-022-14383-8",
    "url": "https://doi.org/10.1038/s41598-022-14383-8",
    "journal": "Scientific Reports",
    "year": 2022,
    "authors": "Rad, N.; Bekker, A.; Arashi, M.",
    "abstract": "AbstractWind energy production depends not only on wind speed but also on wind direction. Thus, predicting and estimating the wind direction for sites accurately will enhance measuring the wind energy potential. The uncertain nature of wind direction can be presented through probability distributions and Bayesian analysis can improve the modeling of the wind direction using the contribution of the prior knowledge to update the empirical shreds of evidence. This must align with the nature of the ",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Renewable Energy Resource Mapping",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "A weighted energy consumption minimization-based multi-hop uneven clustering routing protocol for cognitive radio sensor networks",
    "doi": "10.1038/s41598-022-18310-9",
    "url": "https://doi.org/10.1038/s41598-022-18310-9",
    "journal": "Scientific Reports",
    "year": 2022,
    "authors": "Wang, J.; Li, C.",
    "abstract": "AbstractAiming at solving the effective data delivery and energy hole problem in multi-hop cognitive radio sensor networks (CRSNs), a weighted energy consumption minimization-based uneven clustering (ECMUC) routing protocol is proposed in this paper. For the first time, the impact of control overhead on the network performance is taken into consideration, to be specific, the energy consumption of control overhead is integrated with that of data communication to model the network energy consumpti",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Low-carbon economic dispatch considering integrated demand response and multistep carbon trading for multi-energy microgrid",
    "doi": "10.1038/s41598-022-10123-0",
    "url": "https://doi.org/10.1038/s41598-022-10123-0",
    "journal": "Scientific Reports",
    "year": 2022,
    "authors": "Long, Y.; Li, Y.; Wang, Y.; Cao, Y.; Jiang, L.",
    "abstract": "AbstractWith the rapid development of distributed energy resources and natural gas power generation, multi-energy microgrid (MEMG) is considered as a critical technology to increase the penetration of renewable energy and achieve the target of carbon emission reduction. Therefore, this paper proposes a low-carbon economic dispatch model for MEMG to minimize the daily operation cost by considering integrated demand response (IDR) and multistep carbon trading. Specifically, IDR operation includes ",
    "data_url": "",
    "source": "CrossRef",
    "direction": "EnergiTrade",
    "subcategory": "Energy & Carbon Trading",
    "direction_label": "Carbon Trading & New Business Models",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Performance optimization of monolithic all-perovskite tandem solar cells under standard and real-world solar spectra",
    "doi": "10.1016/j.joule.2022.06.027",
    "url": "https://doi.org/10.1016/j.joule.2022.06.027",
    "journal": "Joule",
    "year": 2022,
    "authors": "Gao, Y.; Lin, R.; Xiao, K.; Luo, X.; Wen, J.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "CleanTech",
    "subcategory": "Solar PV & Storage",
    "direction_label": "Novel Low/Zero Carbon Technologies",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "The demand-side resource opportunity for deep grid decarbonization",
    "doi": "10.1016/j.joule.2022.04.010",
    "url": "https://doi.org/10.1016/j.joule.2022.04.010",
    "journal": "Joule",
    "year": 2022,
    "authors": "O'Shaughnessy, E.; Shah, M.; Parra, D.; Ardani, K.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Extreme weather and electricity markets: Key lessons from the February 2021 Texas crisis",
    "doi": "10.1016/j.joule.2021.12.015",
    "url": "https://doi.org/10.1016/j.joule.2021.12.015",
    "journal": "Joule",
    "year": 2022,
    "authors": "Levin, T.; Botterud, A.; Mann, W.; Kwon, J.; Zhou, Z.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "Carbon Trading & New Business Models",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Understanding battery aging in grid energy storage systems",
    "doi": "10.1016/j.joule.2022.09.014",
    "url": "https://doi.org/10.1016/j.joule.2022.09.014",
    "journal": "Joule",
    "year": 2022,
    "authors": "Kumtepeli, V.; Howey, D.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Policy-driven solar innovation and deployment remains critical for US grid decarbonization",
    "doi": "10.1016/j.joule.2022.07.012",
    "url": "https://doi.org/10.1016/j.joule.2022.07.012",
    "journal": "Joule",
    "year": 2022,
    "authors": "O’Shaughnessy, E.; Ardani, K.; Denholm, P.; Mai, T.; Silverman, T.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "Novel Low/Zero Carbon Technologies",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Global land-use intensity and anthropogenic emissions exhibit symbiotic and explosive behavior",
    "doi": "10.1016/j.isci.2022.104741",
    "url": "https://doi.org/10.1016/j.isci.2022.104741",
    "journal": "iScience",
    "year": 2022,
    "authors": "Sarkodie, S.; Owusu, P.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Demand Response & IoT"
  },
  {
    "title": "Seasonal challenges for a California renewable- energy-driven grid",
    "doi": "10.1016/j.isci.2021.103577",
    "url": "https://doi.org/10.1016/j.isci.2021.103577",
    "journal": "iScience",
    "year": 2022,
    "authors": "Abido, M.; Mahmud, Z.; Sánchez-Pérez, P.; Kurtz, S.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Targeted demand response for mitigating price volatility and enhancing grid reliability in synthetic Texas electricity markets",
    "doi": "10.1016/j.isci.2021.103723",
    "url": "https://doi.org/10.1016/j.isci.2021.103723",
    "journal": "iScience",
    "year": 2022,
    "authors": "Lee, K.; Geng, X.; Sivaranjani, S.; Xia, B.; Ming, H.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "FLEXERGY",
    "subcategory": "Demand Response",
    "direction_label": "Carbon Trading & New Business Models",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Changing sensitivity to cold weather in Texas power demand",
    "doi": "10.1016/j.isci.2022.104173",
    "url": "https://doi.org/10.1016/j.isci.2022.104173",
    "journal": "iScience",
    "year": 2022,
    "authors": "Shaffer, B.; Quintero, D.; Rhodes, J.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Distribution grid impacts of electric vehicles: A California case study",
    "doi": "10.1016/j.isci.2021.103686",
    "url": "https://doi.org/10.1016/j.isci.2021.103686",
    "journal": "iScience",
    "year": 2022,
    "authors": "Jenn, A.; Highleyman, J.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "FLEXERGY",
    "subcategory": "Electric Vehicles & Mobility",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Planning for the evolution of the electric grid with a long-run marginal emission rate",
    "doi": "10.1016/j.isci.2022.103915",
    "url": "https://doi.org/10.1016/j.isci.2022.103915",
    "journal": "iScience",
    "year": 2022,
    "authors": "Gagnon, P.; Cole, W.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Aqueous zinc batteries: Design principles toward organic cathodes for grid applications",
    "doi": "10.1016/j.isci.2022.104204",
    "url": "https://doi.org/10.1016/j.isci.2022.104204",
    "journal": "iScience",
    "year": 2022,
    "authors": "Grignon, E.; Battaglia, A.; Schon, T.; Seferos, D.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Large balancing areas and dispersed renewable investment enhance grid flexibility in a renewable-dominant power system in China",
    "doi": "10.1016/j.isci.2022.103749",
    "url": "https://doi.org/10.1016/j.isci.2022.103749",
    "journal": "iScience",
    "year": 2022,
    "authors": "Lin, J.; Abhyankar, N.; He, G.; Liu, X.; Yin, S.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "FLEXERGY",
    "subcategory": "Demand Response",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Heterogeneous changes in electricity consumption patterns of residential distributed solar consumers due to battery storage adoption",
    "doi": "10.1016/j.isci.2022.104352",
    "url": "https://doi.org/10.1016/j.isci.2022.104352",
    "journal": "iScience",
    "year": 2022,
    "authors": "Qiu, Y.; Xing, B.; Patwardhan, A.; Hultman, N.; Zhang, H.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "Novel Low/Zero Carbon Technologies",
    "refined_category": "Demand Response & IoT"
  },
  {
    "title": "Uncovering the biological basis of control energy: Structural and metabolic correlates of energy inefficiency in temporal lobe epilepsy",
    "doi": "10.1126/sciadv.abn2293",
    "url": "https://doi.org/10.1126/sciadv.abn2293",
    "journal": "Science Advances",
    "year": 2022,
    "authors": "He, X.; Caciagli, L.; Parkes, L.; Stiso, J.; Karrer, T.",
    "abstract": "Network control theory is increasingly used to profile the brain’s energy landscape via simulations of neural dynamics. This approach estimates the control energy required to simulate the activation of brain circuits based on structural connectome measured using diffusion magnetic resonance imaging, thereby quantifying those circuits’ energetic efficiency. The biological basis of control energy, however, remains unknown, hampering its further application. To fill this gap, investigating temporal",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Addressing gain-bandwidth trade-off by a monolithically integrated photovoltaic transistor",
    "doi": "10.1126/sciadv.abq0187",
    "url": "https://doi.org/10.1126/sciadv.abq0187",
    "journal": "Science Advances",
    "year": 2022,
    "authors": "Li, Y.; Chen, G.; Zhao, S.; Liu, C.; Zhao, N.",
    "abstract": "\n            The gain-bandwidth trade-off limits the development of high-performance photodetectors; i.e., the mutual restraint between the response speed and gain has intrinsically limited performance optimization of photomultiplication phototransistors and photodiodes. Here, we show that a monolithically integrated photovoltaic transistor can solve this dilemma. In this structure, the photovoltage generated by the superimposed perovskite solar cell, acting as a float gate, is amplified by the ",
    "data_url": "",
    "source": "CrossRef",
    "direction": "CleanTech",
    "subcategory": "Solar PV & Storage",
    "direction_label": "Novel Low/Zero Carbon Technologies",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Influence of voids on the thermal and light stability of perovskite solar cells",
    "doi": "10.1126/sciadv.abo5977",
    "url": "https://doi.org/10.1126/sciadv.abo5977",
    "journal": "Science Advances",
    "year": 2022,
    "authors": "Wang, M.; Fei, C.; Uddin, M.; Huang, J.",
    "abstract": "The formation of voids in perovskite films close to the buried interface has been reported during film deposition. These voids are thought to limits the efficiency and stability of perovskite solar cells (PSCs). Here, we studied the voids formed during operation in perovskite films that were optimized during the solution deposition process to avoid voids. New voids formed during operation are found to assemble along grain boundaries at the bottom interface, caused by the loss of residual solvent",
    "data_url": "",
    "source": "CrossRef",
    "direction": "CleanTech",
    "subcategory": "Solar PV & Storage",
    "direction_label": "Novel Low/Zero Carbon Technologies",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "The effect of renewable energy incorporation on power grid stability and resilience",
    "doi": "10.1126/sciadv.abj6734",
    "url": "https://doi.org/10.1126/sciadv.abj6734",
    "journal": "Science Advances",
    "year": 2022,
    "authors": "Smith, O.; Cattell, O.; Farcot, E.; O’Dea, R.; Hopcraft, K.",
    "abstract": "Contemporary proliferation of renewable power generation is causing an overhaul in the topology, composition, and dynamics of electrical grids. These low-output, intermittent generators are widely distributed throughout the grid, including at the household level. It is critical for the function of modern power infrastructure to understand how this increasingly distributed layout affects network stability and resilience. This paper uses dynamical models, household power consumption, and photovolt",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Safer carbon nanotube processing expands industrial and consumer applications",
    "doi": "10.1126/sciadv.abq4853",
    "url": "https://doi.org/10.1126/sciadv.abq4853",
    "journal": "Science Advances",
    "year": 2022,
    "authors": "Lowery, J.; Green, M.",
    "abstract": "Safer, less-reactive superacid processing enables printing and coating of carbon nanotubes into films, fibers, and fabrics.",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Demand Response & IoT"
  },
  {
    "title": "A synthetic building operation dataset",
    "doi": "10.1038/s41597-021-00989-6",
    "url": "https://doi.org/10.1038/s41597-021-00989-6",
    "journal": "Scientific Data",
    "year": 2021,
    "authors": "Li, H.; Wang, Z.; Hong, T.",
    "abstract": "AbstractThis paper presents a synthetic building operation dataset which includes HVAC, lighting, miscellaneous electric loads (MELs) system operating conditions, occupant counts, environmental parameters, end-use and whole-building energy consumptions at 10-minute intervals. The data is created with 1395 annual simulations using the U.S. DOE detailed medium-sized reference office building, and 30 years’ historical weather data in three typical climates including Miami, San Francisco, and Chicag",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "DEDDIAG, a domestic electricity demand dataset of individual appliances in Germany",
    "doi": "10.1038/s41597-021-00963-2",
    "url": "https://doi.org/10.1038/s41597-021-00963-2",
    "journal": "Scientific Data",
    "year": 2021,
    "authors": "Wenninger, M.; Maier, A.; Schmidt, J.",
    "abstract": "AbstractReal-world domestic electricity demand datasets are the key enabler for developing and evaluating machine learning algorithms that facilitate the analysis of demand attribution and usage behavior. Breaking down the electricity demand of domestic households is seen as the key technology for intelligent smart-grid management systems that seek an equilibrium of electricity supply and demand. For the purpose of comparable research, we publish DEDDIAG, a domestic electricity demand dataset of",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "An open tool for creating battery-electric vehicle time series from empirical data, emobpy",
    "doi": "10.1038/s41597-021-00932-9",
    "url": "https://doi.org/10.1038/s41597-021-00932-9",
    "journal": "Scientific Data",
    "year": 2021,
    "authors": "Gaete-Morales, C.; Kramer, H.; Schill, W.; Zerrahn, A.",
    "abstract": "AbstractThere is substantial research interest in how future fleets of battery-electric vehicles will interact with the power sector. Various types of energy models are used for respective analyses. They depend on meaningful input parameters, in particular time series of vehicle mobility, driving electricity consumption, grid availability, or grid electricity demand. As the availability of such data is highly limited, we introduce the open-source tool emobpy. Based on mobility statistics, physic",
    "data_url": "",
    "source": "CrossRef",
    "direction": "FLEXERGY",
    "subcategory": "Electric Vehicles & Mobility",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Time series of useful energy consumption patterns for energy system modeling",
    "doi": "10.1038/s41597-021-00907-w",
    "url": "https://doi.org/10.1038/s41597-021-00907-w",
    "journal": "Scientific Data",
    "year": 2021,
    "authors": "Priesmann, J.; Nolting, L.; Kockel, C.; Praktiknjo, A.",
    "abstract": "AbstractThe analysis of energy scenarios for future energy systems requires appropriate data. However, while more or less detailed data on energy production is often available, appropriate data on energy consumption is often scarce. In our JERICHO-E-usage dataset, we provide comprehensive data on useful energy consumption patterns for heat, cold, mechanical energy, information and communication, and light in high spatial and temporal resolution. Furthermore, we distinguish between residential, i",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Renewable Energy Simulation Tools",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Truck electrification has minor grid impacts",
    "doi": "10.1038/s41560-021-00857-y",
    "url": "https://doi.org/10.1038/s41560-021-00857-y",
    "journal": "Nature Energy",
    "year": 2021,
    "authors": "Liimatainen, H.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Cleaning cars, grid and air",
    "doi": "10.1038/s41560-020-00769-3",
    "url": "https://doi.org/10.1038/s41560-020-00769-3",
    "journal": "Nature Energy",
    "year": 2021,
    "authors": "Smith, S.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Inequality built into the grid",
    "doi": "10.1038/s41560-021-00873-y",
    "url": "https://doi.org/10.1038/s41560-021-00873-y",
    "journal": "Nature Energy",
    "year": 2021,
    "authors": "Moreno-Munoz, A.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Inequitable access to distributed energy resources due to grid infrastructure limits in California",
    "doi": "10.1038/s41560-021-00887-6",
    "url": "https://doi.org/10.1038/s41560-021-00887-6",
    "journal": "Nature Energy",
    "year": 2021,
    "authors": "Brockway, A.; Conde, J.; Callaway, D.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "Carbon Trading & New Business Models",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Economic, environmental and grid-resilience benefits of converting diesel trains to battery-electric",
    "doi": "10.1038/s41560-021-00915-5",
    "url": "https://doi.org/10.1038/s41560-021-00915-5",
    "journal": "Nature Energy",
    "year": 2021,
    "authors": "Popovich, N.; Rajagopal, D.; Tasar, E.; Phadke, A.",
    "abstract": "Abstract\n                  \n                    Nearly all US locomotives are propelled by diesel-electric drives, which emit 35 million tonnes of CO\n                    2\n                    and produce air pollution causing about 1,000 premature deaths annually, accounting for approximately US$6.5 billion in annual health damage costs. Improved battery technology plus access to cheap renewable electricity open the possibility of battery-electric rail. Here we show that a 241-km range can be ac",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "High resolution global spatiotemporal assessment of rooftop solar photovoltaics potential for renewable electricity generation",
    "doi": "10.1038/s41467-021-25720-2",
    "url": "https://doi.org/10.1038/s41467-021-25720-2",
    "journal": "Nature Communications",
    "year": 2021,
    "authors": "Joshi, S.; Mittal, S.; Holloway, P.; Shukla, P.; Ó Gallachóir, B.",
    "abstract": "AbstractRooftop solar photovoltaics currently account for 40% of the global solar photovoltaics installed capacity and one-fourth of the total renewable capacity additions in 2018. Yet, only limited information is available on its global potential and associated costs at a high spatiotemporal resolution. Here, we present a high-resolution global assessment of rooftop solar photovoltaics potential using big data, machine learning and geospatial analysis. We analyse 130 million km2of global land s",
    "data_url": "",
    "source": "CrossRef",
    "direction": "CleanTech",
    "subcategory": "Solar PV & Storage",
    "direction_label": "Novel Low/Zero Carbon Technologies",
    "refined_category": "Demand Response & IoT"
  },
  {
    "title": "Linear reinforcement learning in planning, grid fields, and cognitive control",
    "doi": "10.1038/s41467-021-25123-3",
    "url": "https://doi.org/10.1038/s41467-021-25123-3",
    "journal": "Nature Communications",
    "year": 2021,
    "authors": "Piray, P.; Daw, N.",
    "abstract": "Abstract\n                  It is thought that the brain’s judicious reuse of previous computation underlies our ability to plan flexibly, but also that inappropriate reuse gives rise to inflexibilities like habits and compulsion. Yet we lack a complete, realistic account of either. Building on control engineering, here we introduce a model for decision making in the brain that reuses a temporally abstracted map of future events to enable biologically-realistic, flexible choice at the expense of ",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Perineuronal nets stabilize the grid cell network",
    "doi": "10.1038/s41467-020-20241-w",
    "url": "https://doi.org/10.1038/s41467-020-20241-w",
    "journal": "Nature Communications",
    "year": 2021,
    "authors": "Christensen, A.; Lensjø, K.; Lepperød, M.; Dragly, S.; Sutterud, H.",
    "abstract": "Abstract\n                  Grid cells are part of a widespread network which supports navigation and spatial memory. Stable grid patterns appear late in development, in concert with extracellular matrix aggregates termed perineuronal nets (PNNs) that condense around inhibitory neurons. It has been suggested that PNNs stabilize synaptic connections and long-term memories, but their role in the grid cell network remains elusive. We show that removal of PNNs leads to lower inhibitory spiking activi",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "Carbon Trading & New Business Models",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "A frequency-amplitude coordinator and its optimal energy consumption for biological oscillators",
    "doi": "10.1038/s41467-021-26182-2",
    "url": "https://doi.org/10.1038/s41467-021-26182-2",
    "journal": "Nature Communications",
    "year": 2021,
    "authors": "Qin, B.; Zhao, L.; Lin, W.",
    "abstract": "AbstractBiorhythm including neuron firing and protein-mRNA interaction are fundamental activities with diffusive effect. Their well-balanced spatiotemporal dynamics are beneficial for healthy sustainability. Therefore, calibrating both anomalous frequency and amplitude of biorhythm prevents physiological dysfunctions or diseases. However, many works were devoted to modulate frequency exclusively whereas amplitude is usually ignored, although both quantities are equally significant for coordinati",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Demand Response & IoT"
  },
  {
    "title": "On-demand synthesis of phosphoramidites",
    "doi": "10.1038/s41467-021-22945-z",
    "url": "https://doi.org/10.1038/s41467-021-22945-z",
    "journal": "Nature Communications",
    "year": 2021,
    "authors": "Sandahl, A.; Nguyen, T.; Hansen, R.; Johansen, M.; Skrydstrup, T.",
    "abstract": "Abstract\n                  Automated chemical synthesis of oligonucleotides is of fundamental importance for the production of primers for the polymerase chain reaction (PCR), for oligonucleotide-based drugs, and for numerous other medical and biotechnological applications. The highly optimised automised chemical oligonucleotide synthesis relies upon phosphoramidites as the phosphate precursors and one of the drawbacks of this technology is the poor bench stability of phosphoramidites. Here, we ",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Application of large-scale grid-connected solar photovoltaic system for voltage stability improvement of weak national grids",
    "doi": "10.1038/s41598-021-04300-w",
    "url": "https://doi.org/10.1038/s41598-021-04300-w",
    "journal": "Scientific Reports",
    "year": 2021,
    "authors": "Adetokun, B.; Ojo, J.; Muriithi, C.",
    "abstract": "AbstractThis paper investigates the application of large-scale solar photovoltaic (SPV) system for voltage stability improvement of weak national grids. Large-scale SPV integration has been investigated on the Nigerian power system to enhance voltage stability and as a viable alternative to the aged shunt reactors currently being used in the Nigerian national grid to mitigate overvoltage issues in Northern Nigeria. Two scenarios of increasing SPV penetration level (PL) are investigated in this w",
    "data_url": "",
    "source": "CrossRef",
    "direction": "CleanTech",
    "subcategory": "Solar PV & Storage",
    "direction_label": "Novel Low/Zero Carbon Technologies",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Estimation of carbon dioxide emissions from the megafires of Australia in 2019–2020",
    "doi": "10.1038/s41598-021-87721-x",
    "url": "https://doi.org/10.1038/s41598-021-87721-x",
    "journal": "Scientific Reports",
    "year": 2021,
    "authors": "Shiraishi, T.; Hirata, R.",
    "abstract": "AbstractCatastrophic fires occurred in Australia between 2019 and 2020. These fires burned vast areas and caused extensive damage to the environment and wildlife. In this study, we estimated the carbon dioxide (CO2) emissions from these fires using a bottom-up method involving the improved burnt area approach and up-to-date remote sensing datasets to create monthly time series distribution maps for Australia from January 2019 to February 2020. The highest monthly CO2 emissions in Australia since",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "Carbon Trading & New Business Models",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Controlled synthesis of various Fe2O3 morphologies as energy storage materials",
    "doi": "10.1038/s41598-021-84755-z",
    "url": "https://doi.org/10.1038/s41598-021-84755-z",
    "journal": "Scientific Reports",
    "year": 2021,
    "authors": "Hang, B.; Anh, T.",
    "abstract": "AbstractAir pollution from vehicle emissions is a major problem in developing countries. Consequently, the use of iron-based rechargeable batteries, which is an effective method of reducing air pollution, have been extensively studied for electric vehicles. The structures and morphologies of iron particles significantly affect the cycle performance of iron-based rechargeable batteries. The synthesis parameters for these iron materials also remarkably influence their structures, shapes, sizes, an",
    "data_url": "",
    "source": "CrossRef",
    "direction": "FLEXERGY",
    "subcategory": "Electric Vehicles & Mobility",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Geomechanical simulation of energy storage in salt formations",
    "doi": "10.1038/s41598-021-99161-8",
    "url": "https://doi.org/10.1038/s41598-021-99161-8",
    "journal": "Scientific Reports",
    "year": 2021,
    "authors": "Ramesh Kumar, K.; Makhmutov, A.; Spiers, C.; Hajibeygi, H.",
    "abstract": "AbstractA promising option for storing large-scale quantities of green gases (e.g., hydrogen) is in subsurface rock salt caverns. The mechanical performance of salt caverns utilized for long-term subsurface energy storage plays a significant role in long-term stability and serviceability. However, rock salt undergoes non-linear creep deformation due to long-term loading caused by subsurface storage. Salt caverns have complex geometries and the geological domain surrounding salt caverns has a vas",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Energy budget and carbon footprint in a wheat and maize system under ridge furrow strategy in dry semi humid areas",
    "doi": "10.1038/s41598-021-88717-3",
    "url": "https://doi.org/10.1038/s41598-021-88717-3",
    "journal": "Scientific Reports",
    "year": 2021,
    "authors": "Li, C.; Li, S.",
    "abstract": "AbstractThe well-irrigated planting strategy (WI) consumes a large amount of energy and exacerbates greenhouse gas emissions, endangering the sustainable agricultural production. This 2-year work aims to estimate the economic benefit, energy budget and carbon footprint of a wheat–maize double cropping system under conventional rain-fed flat planting (irrigation once a year, control), ridge–furrows with plastic film mulching on the ridge (irrigation once a year, RP), and the WI in dry semi-humid ",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "Carbon Trading & New Business Models",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Adaptive optimal allocation of water resources response to future water availability and water demand in the Han River basin, China",
    "doi": "10.1038/s41598-021-86961-1",
    "url": "https://doi.org/10.1038/s41598-021-86961-1",
    "journal": "Scientific Reports",
    "year": 2021,
    "authors": "Tian, J.; Guo, S.; Deng, L.; Yin, J.; Pan, Z.",
    "abstract": "AbstractGlobal warming and anthropogenic changes can result in the heterogeneity of water availability in the spatiotemporal scale, which will further affect the allocation of water resources. A lot of researches have been devoted to examining the responses of water availability to global warming while neglected future anthropogenic changes. What’s more, only a few studies have investigated the response of optimal allocation of water resources to the projected climate and anthropogenic changes. ",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Demand Response & IoT"
  },
  {
    "title": "Techno-economic analysis of long-duration energy storage and flexible power generation technologies to support high-variable renewable energy grids",
    "doi": "10.1016/j.joule.2021.06.018",
    "url": "https://doi.org/10.1016/j.joule.2021.06.018",
    "journal": "Joule",
    "year": 2021,
    "authors": "Hunter, C.; Penev, M.; Reznicek, E.; Eichman, J.; Rustagi, N.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Rwanda’s Off-Grid Solar Performance Targets",
    "doi": "10.1016/j.joule.2020.12.016",
    "url": "https://doi.org/10.1016/j.joule.2020.12.016",
    "journal": "Joule",
    "year": 2021,
    "authors": "Asemota, G.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "Novel Low/Zero Carbon Technologies",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Building and grid system benefits of demand flexibility and energy efficiency",
    "doi": "10.1016/j.joule.2021.08.001",
    "url": "https://doi.org/10.1016/j.joule.2021.08.001",
    "journal": "Joule",
    "year": 2021,
    "authors": "Jackson, R.; Zhou, E.; Reyna, J.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "US building energy efficiency and flexibility as an electric grid resource",
    "doi": "10.1016/j.joule.2021.06.002",
    "url": "https://doi.org/10.1016/j.joule.2021.06.002",
    "journal": "Joule",
    "year": 2021,
    "authors": "Langevin, J.; Harris, C.; Satre-Meloy, A.; Chandra-Putra, H.; Speake, A.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Predicting battery end of life from solar off-grid system field data using machine learning",
    "doi": "10.1016/j.joule.2021.11.006",
    "url": "https://doi.org/10.1016/j.joule.2021.11.006",
    "journal": "Joule",
    "year": 2021,
    "authors": "Aitio, A.; Howey, D.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "Novel Low/Zero Carbon Technologies",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Toward carbon-neutral electricity and mobility: Is the grid infrastructure ready?",
    "doi": "10.1016/j.joule.2021.06.011",
    "url": "https://doi.org/10.1016/j.joule.2021.06.011",
    "journal": "Joule",
    "year": 2021,
    "authors": "Xie, L.; Singh, C.; Mitter, S.; Dahleh, M.; Oren, S.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Solar and wind grid system value in the United States: The effect of transmission congestion, generation profiles, and curtailment",
    "doi": "10.1016/j.joule.2021.05.009",
    "url": "https://doi.org/10.1016/j.joule.2021.05.009",
    "journal": "Joule",
    "year": 2021,
    "authors": "Millstein, D.; Wiser, R.; Mills, A.; Bolinger, M.; Seel, J.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "Novel Low/Zero Carbon Technologies",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "From silos to systems: Enabling off-grid electrification of healthcare facilities, households, and businesses in sub-Saharan Africa",
    "doi": "10.1016/j.oneear.2021.10.021",
    "url": "https://doi.org/10.1016/j.oneear.2021.10.021",
    "journal": "One Earth",
    "year": 2021,
    "authors": "Trotter, P.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Cryo-EM grid optimization for membrane proteins",
    "doi": "10.1016/j.isci.2021.102139",
    "url": "https://doi.org/10.1016/j.isci.2021.102139",
    "journal": "iScience",
    "year": 2021,
    "authors": "Kampjut, D.; Steiner, J.; Sazanov, L.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Modularization of grid cells constrained by the pyramidal patch lattice",
    "doi": "10.1016/j.isci.2021.102301",
    "url": "https://doi.org/10.1016/j.isci.2021.102301",
    "journal": "iScience",
    "year": 2021,
    "authors": "Wang, T.; Yang, F.; Wang, Z.; Zhang, B.; Wang, W.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Comprehensive early warning strategies based on consistency deviation of thermal–electrical characteristics for energy storage grid",
    "doi": "10.1016/j.isci.2021.103058",
    "url": "https://doi.org/10.1016/j.isci.2021.103058",
    "journal": "iScience",
    "year": 2021,
    "authors": "Wu, X.; Cui, Z.; Zhou, G.; Wen, T.; Hu, F.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "A high-performance triboelectric-electromagnetic hybrid wind energy harvester based on rotational tapered rollers aiming at outdoor IoT applications",
    "doi": "10.1016/j.isci.2021.102300",
    "url": "https://doi.org/10.1016/j.isci.2021.102300",
    "journal": "iScience",
    "year": 2021,
    "authors": "Fang, Y.; Tang, T.; Li, Y.; Hou, C.; Wen, F.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Demand Response & IoT"
  },
  {
    "title": "Machine learning toward advanced energy storage devices and systems",
    "doi": "10.1016/j.isci.2020.101936",
    "url": "https://doi.org/10.1016/j.isci.2020.101936",
    "journal": "iScience",
    "year": 2021,
    "authors": "Gao, T.; Lu, W.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "AI & Deep Learning"
  },
  {
    "title": "China's vehicle electrification impacts on sales, fuel use, and battery material demand through 2050: Optimizing consumer and industry decisions",
    "doi": "10.1016/j.isci.2021.103375",
    "url": "https://doi.org/10.1016/j.isci.2021.103375",
    "journal": "iScience",
    "year": 2021,
    "authors": "Ou, S.; Hsieh, I.; He, X.; Lin, Z.; Yu, R.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Demand Response & IoT"
  },
  {
    "title": "Controlling electrochemical growth of metallic zinc electrodes: Toward affordable rechargeable energy storage systems",
    "doi": "10.1126/sciadv.abe0219",
    "url": "https://doi.org/10.1126/sciadv.abe0219",
    "journal": "Science Advances",
    "year": 2021,
    "authors": "Zheng, J.; Archer, L.",
    "abstract": "Zinc anodes are a powerful platform for understanding metal deposition and for low-cost electrical energy storage.",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Theta oscillations coordinate grid-like representations between ventromedial prefrontal and entorhinal cortex",
    "doi": "10.1126/sciadv.abj0200",
    "url": "https://doi.org/10.1126/sciadv.abj0200",
    "journal": "Science Advances",
    "year": 2021,
    "authors": "Chen, D.; Kunz, L.; Lv, P.; Zhang, H.; Zhou, W.",
    "abstract": "Human iEEG reveals synchronous theta oscillations and coordinated grid-like representations between vmPFC and entorhinal cortex.",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Optogenetic pacing of medial septum parvalbumin-positive cells disrupts temporal but not spatial firing in grid cells",
    "doi": "10.1126/sciadv.abd5684",
    "url": "https://doi.org/10.1126/sciadv.abd5684",
    "journal": "Science Advances",
    "year": 2021,
    "authors": "Lepperød, M.; Christensen, A.; Lensjø, K.; Buccino, A.; Yu, J.",
    "abstract": "Spatial code of grid cells is independent of theta oscillations and phase precession.",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "A harmonised, high-coverage, open dataset of solar photovoltaic installations in the UK",
    "doi": "10.1038/s41597-020-00739-0",
    "url": "https://doi.org/10.1038/s41597-020-00739-0",
    "journal": "Scientific Data",
    "year": 2020,
    "authors": "Stowell, D.; Kelly, J.; Tanner, D.; Taylor, J.; Jones, E.",
    "abstract": "AbstractSolar photovoltaic (PV) is an increasingly significant fraction of electricity generation. Efficient management, and innovations such as short-term forecasting and machine vision, demand high-resolution geographic datasets of PV installations. However, official and public sources have notable deficiencies: spatial imprecision, gaps in coverage and lack of crucial meta data, especially for small-scale solar panel installations. We present the results of a major crowd-sourcing campaign to ",
    "data_url": "",
    "source": "CrossRef",
    "direction": "CleanTech",
    "subcategory": "Solar PV & Storage",
    "direction_label": "Novel Low/Zero Carbon Technologies",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "A synthetic energy dataset for non-intrusive load monitoring in households",
    "doi": "10.1038/s41597-020-0434-6",
    "url": "https://doi.org/10.1038/s41597-020-0434-6",
    "journal": "Scientific Data",
    "year": 2020,
    "authors": "Klemenjak, C.; Kovatsch, C.; Herold, M.; Elmenreich, W.",
    "abstract": "AbstractResearch on smart grid technologies is expected to result in effective climate change mitigation. Non-Intrusive Load Monitoring (NILM) is seen as a key technique for enabling innovative smart-grid services. By breaking down the energy consumption of households and industrial facilities into its components, NILM techniques provide information on present appliances and can be applied to perform diagnostics. As with related Machine Learning problems, research and development requires a suff",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Developing reliable hourly electricity demand data through screening and imputation",
    "doi": "10.1038/s41597-020-0483-x",
    "url": "https://doi.org/10.1038/s41597-020-0483-x",
    "journal": "Scientific Data",
    "year": 2020,
    "authors": "Ruggles, T.; Farnham, D.; Tong, D.; Caldeira, K.",
    "abstract": "AbstractElectricity usage (demand) data are used by utilities, governments, and academics to model electric grids for a variety of planning (e.g., capacity expansion and system operation) purposes. The U.S. Energy Information Administration collects hourly demand data from all balancing authorities (BAs) in the contiguous United States. As of September 2019, we find 2.2% of the demand data in their database are missing. Additionally, 0.5% of reported quantities are either negative values or are ",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset",
    "doi": "10.1038/s41597-020-0453-3",
    "url": "https://doi.org/10.1038/s41597-020-0453-3",
    "journal": "Scientific Data",
    "year": 2020,
    "authors": "Harris, I.; Osborn, T.; Jones, P.; Lister, D.",
    "abstract": "Abstract\n                  CRU TS (Climatic Research Unit gridded Time Series) is a widely used climate dataset on a 0.5° latitude by 0.5° longitude grid over all land domains of the world except Antarctica. It is derived by the interpolation of monthly climate anomalies from extensive networks of weather station observations. Here we describe the construction of a major new version, CRU TS v4. It is updated to span 1901–2018 by the inclusion of additional station observations, and it will be up",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Machine learning model to project the impact of COVID-19 on US motor gasoline demand",
    "doi": "10.1038/s41560-020-0662-1",
    "url": "https://doi.org/10.1038/s41560-020-0662-1",
    "journal": "Nature Energy",
    "year": 2020,
    "authors": "Ou, S.; He, X.; Ji, W.; Chen, W.; Sui, L.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "AI & Deep Learning"
  },
  {
    "title": "Author Correction: Machine learning model to project the impact of COVID-19 on US motor gasoline demand",
    "doi": "10.1038/s41560-020-00711-7",
    "url": "https://doi.org/10.1038/s41560-020-00711-7",
    "journal": "Nature Energy",
    "year": 2020,
    "authors": "Ou, S.; He, X.; Ji, W.; Chen, W.; Sui, L.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "AI & Deep Learning"
  },
  {
    "title": "Cleaning the grid",
    "doi": "10.1038/s41893-020-00663-6",
    "url": "https://doi.org/10.1038/s41893-020-00663-6",
    "journal": "Nature Sustainability",
    "year": 2020,
    "authors": "Millstein, D.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Re-evaluating effectiveness of vehicle emission control programmes targeting high-emitters",
    "doi": "10.1038/s41893-020-0573-y",
    "url": "https://doi.org/10.1038/s41893-020-0573-y",
    "journal": "Nature Sustainability",
    "year": 2020,
    "authors": "Huang, Y.; Surawski, N.; Yam, Y.; Lee, C.; Zhou, J.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Electricity-powered artificial root nodule",
    "doi": "10.1038/s41467-020-15314-9",
    "url": "https://doi.org/10.1038/s41467-020-15314-9",
    "journal": "Nature Communications",
    "year": 2020,
    "authors": "Lu, S.; Guan, X.; Liu, C.",
    "abstract": "AbstractRoot nodules are agricultural-important symbiotic plant-microbe composites in which microorganisms receive energy from plants and reduce dinitrogen (N2) into fertilizers. Mimicking root nodules using artificial devices can enable renewable energy-driven fertilizer production. This task is challenging due to the necessity of a microscopic dioxygen (O2) concentration gradient, which reconciles anaerobic N2 fixation with O2-rich atmosphere. Here we report our designed electricity-powered bi",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Demand Response & IoT"
  },
  {
    "title": "Anomalous supply shortages from dynamic pricing in on-demand mobility",
    "doi": "10.1038/s41467-020-18370-3",
    "url": "https://doi.org/10.1038/s41467-020-18370-3",
    "journal": "Nature Communications",
    "year": 2020,
    "authors": "Schröder, M.; Storch, D.; Marszal, P.; Timme, M.",
    "abstract": "AbstractDynamic pricing schemes are increasingly employed across industries to maintain a self-organized balance of demand and supply. However, throughout complex dynamical systems, unintended collective states exist that may compromise their function. Here we reveal how dynamic pricing may induce demand-supply imbalances instead of preventing them. Combining game theory and time series analysis of dynamic pricing data from on-demand ride-hailing services, we explain this apparent contradiction.",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Grid cells are modulated by local head direction",
    "doi": "10.1038/s41467-020-17500-1",
    "url": "https://doi.org/10.1038/s41467-020-17500-1",
    "journal": "Nature Communications",
    "year": 2020,
    "authors": "Gerlei, K.; Passlack, J.; Hawes, I.; Vandrey, B.; Stevens, H.",
    "abstract": "AbstractGrid and head direction codes represent cognitive spaces for navigation and memory. Pure grid cells generate grid codes that have been assumed to be independent of head direction, whereas conjunctive cells generate grid representations that are tuned to a single head direction. Here, we demonstrate that pure grid cells also encode head direction, but through distinct mechanisms. We show that individual firing fields of pure grid cells are tuned to multiple head directions, with the prefe",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "India’s potential for integrating solar and on- and offshore wind power into its energy system",
    "doi": "10.1038/s41467-020-18318-7",
    "url": "https://doi.org/10.1038/s41467-020-18318-7",
    "journal": "Nature Communications",
    "year": 2020,
    "authors": "Lu, T.; Sherman, P.; Chen, X.; Chen, S.; Lu, X.",
    "abstract": "AbstractThis paper considers options for a future Indian power economy in which renewables, wind and solar, could meet 80% of anticipated 2040 power demand supplanting the country’s current reliance on coal. Using a cost optimization model, here we show that renewables could provide a source of power cheaper or at least competitive with what could be supplied using fossil-based alternatives. The ancillary advantage would be a significant reduction in India’s future power sector related emissions",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Renewable Energy Resource Mapping",
    "direction_label": "Novel Low/Zero Carbon Technologies",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "A wind-albedo-wind feedback driven by landscape evolution",
    "doi": "10.1038/s41467-019-13661-w",
    "url": "https://doi.org/10.1038/s41467-019-13661-w",
    "journal": "Nature Communications",
    "year": 2020,
    "authors": "Abell, J.; Pullen, A.; Lebo, Z.; Kapp, P.; Gloege, L.",
    "abstract": "AbstractThe accurate characterization of near-surface winds is critical to our understanding of past and modern climate. Dust lofted by these winds has the potential to modify surface and atmospheric conditions as well as ocean biogeochemistry. Stony deserts, low dust emitting regions today, represent expansive areas where variations in surficial geology through time may drastically impact near-surface conditions. Here we use the Weather Research and Forecasting (WRF) model over the western Gobi",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Vicinal difunctionalization of carbon–carbon double bond for the platform synthesis of trifluoroalkyl amines",
    "doi": "10.1038/s41467-020-19748-z",
    "url": "https://doi.org/10.1038/s41467-020-19748-z",
    "journal": "Nature Communications",
    "year": 2020,
    "authors": "Béke, F.; Mészáros, Á.; Tóth, Á.; Botlik, B.; Novák, Z.",
    "abstract": "AbstractRegioselective vicinal diamination of carbon–carbon double bonds with two different amines is a synthetic challenge under transition metal-free conditions, especially for the synthesis of trifluoromethylated amines. However, the synthesis of ethylene diamines and fluorinated amine compounds is demanded, especially in the pharmaceutical sector. Herein, we demonstrate that the controllable double nucleophilic functionalization of an activated alkene synthon, originated from a trifluoroprop",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Prefrontal reinstatement of contextual task demand is predicted by separable hippocampal patterns",
    "doi": "10.1038/s41467-020-15928-z",
    "url": "https://doi.org/10.1038/s41467-020-15928-z",
    "journal": "Nature Communications",
    "year": 2020,
    "authors": "Jiang, J.; Wang, S.; Guo, W.; Fernandez, C.; Wagner, A.",
    "abstract": "AbstractGoal-directed behavior requires the representation of a task-set that defines the task-relevance of stimuli and guides stimulus-action mappings. Past experience provides one source of knowledge about likely task demands in the present, with learning enabling future predictions about anticipated demands. We examine whether spatial contexts serve to cue retrieval of associated task demands (e.g., context A and B probabilistically cue retrieval of task demands X and Y, respectively), and th",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Cost of wind energy generation should include energy storage allowance",
    "doi": "10.1038/s41598-020-59936-x",
    "url": "https://doi.org/10.1038/s41598-020-59936-x",
    "journal": "Scientific Reports",
    "year": 2020,
    "authors": "Boretti, A.; Castelletto, S.",
    "abstract": "AbstractThe statistic of wind energy in the US is presently based on annual average capacity factors, and construction cost (CAPEX). This approach suffers from one major downfall, as it does not include any parameter describing the variability of the wind energy generation. As a grid wind and solar only requires significant storage in terms of both power and energy to compensate for the variability of the resource, there is a need to account also for a parameter describing the variability of the",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "Novel Low/Zero Carbon Technologies",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Mitigating Curtailment and Carbon Emissions through Load Migration between Data Centers",
    "doi": "10.1016/j.joule.2020.08.001",
    "url": "https://doi.org/10.1016/j.joule.2020.08.001",
    "journal": "Joule",
    "year": 2020,
    "authors": "Zheng, J.; Chien, A.; Suh, S.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Reducing Emissions and Costs with Vehicle-to-Grid",
    "doi": "10.1016/j.joule.2020.08.003",
    "url": "https://doi.org/10.1016/j.joule.2020.08.003",
    "journal": "Joule",
    "year": 2020,
    "authors": "Sutherland, B.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "FLEXERGY",
    "subcategory": "Electric Vehicles & Mobility",
    "direction_label": "Demand Response & New Mobilities & Urban Planning",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Electricity Load Implications of Space Heating Decarbonization Pathways",
    "doi": "10.1016/j.joule.2019.11.011",
    "url": "https://doi.org/10.1016/j.joule.2019.11.011",
    "journal": "Joule",
    "year": 2020,
    "authors": "Waite, M.; Modi, V.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Forecasting & Prediction"
  },
  {
    "title": "Securing Smart Grids with Machine Learning",
    "doi": "10.1016/j.joule.2020.02.013",
    "url": "https://doi.org/10.1016/j.joule.2020.02.013",
    "journal": "Joule",
    "year": 2020,
    "authors": "Sutherland, B.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Toward Controlled Thermal Energy Storage and Release in Organic Phase Change Materials",
    "doi": "10.1016/j.joule.2020.07.011",
    "url": "https://doi.org/10.1016/j.joule.2020.07.011",
    "journal": "Joule",
    "year": 2020,
    "authors": "Gerkman, M.; Han, G.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "CleanTech",
    "subcategory": "Building Energy Materials",
    "direction_label": "Novel Low/Zero Carbon Technologies",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Ecosystem services at risk: integrating spatiotemporal dynamics of supply and demand to promote long-term provision",
    "doi": "10.1016/j.oneear.2020.11.003",
    "url": "https://doi.org/10.1016/j.oneear.2020.11.003",
    "journal": "One Earth",
    "year": 2020,
    "authors": "Boesing, A.; Prist, P.; Barreto, J.; Hohlenwerger, C.; Maron, M.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Demand Response & IoT"
  },
  {
    "title": "Impacts of Green New Deal Energy Plans on Grid Stability, Costs, Jobs, Health, and Climate in 143 Countries",
    "doi": "10.1016/j.oneear.2020.01.007",
    "url": "https://doi.org/10.1016/j.oneear.2020.01.007",
    "journal": "One Earth",
    "year": 2020,
    "authors": "Jacobson, M.; Delucchi, M.; Cameron, M.; Coughlin, S.; Hay, C.",
    "abstract": "",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Chemical anti-corrosion strategy for stable inverted perovskite solar cells",
    "doi": "10.1126/sciadv.abd1580",
    "url": "https://doi.org/10.1126/sciadv.abd1580",
    "journal": "Science Advances",
    "year": 2020,
    "authors": "Li, X.; Fu, S.; Zhang, W.; Ke, S.; Song, W.",
    "abstract": "Chemical anticorrosion of metal electrode with benzotriazole inhibitor enhances the stability of perovskite solar cells.",
    "data_url": "",
    "source": "CrossRef",
    "direction": "CleanTech",
    "subcategory": "Solar PV & Storage",
    "direction_label": "Novel Low/Zero Carbon Technologies",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Controlling colloidal crystals via morphing energy landscapes and reinforcement learning",
    "doi": "10.1126/sciadv.abd6716",
    "url": "https://doi.org/10.1126/sciadv.abd6716",
    "journal": "Science Advances",
    "year": 2020,
    "authors": "Zhang, J.; Yang, J.; Zhang, Y.; Bevan, M.",
    "abstract": "A feedback control scheme is developed for rapid assembly of perfect target structures on morphing energy landscapes.",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Voltage controlled on-demand magnonic nanochannels",
    "doi": "10.1126/sciadv.aba5457",
    "url": "https://doi.org/10.1126/sciadv.aba5457",
    "journal": "Science Advances",
    "year": 2020,
    "authors": "Choudhury, S.; Chaurasiya, A.; Mondal, A.; Rana, B.; Miura, K.",
    "abstract": "On-demand magnonic nanochannels are achieved by modulation of voltage-controlled magnetic anisotropy in CoFeB/MgO heterostructure.",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  },
  {
    "title": "Grid diagrams as tools to investigate knot spaces and topoisomerase-mediated simplification of DNA topology",
    "doi": "10.1126/sciadv.aay1458",
    "url": "https://doi.org/10.1126/sciadv.aay1458",
    "journal": "Science Advances",
    "year": 2020,
    "authors": "Barbensi, A.; Celoria, D.; Harrington, H.; Stasiak, A.; Buck, D.",
    "abstract": "Grid diagrams grasp the principle of DNA topology simplification by DNA topoisomerases.",
    "data_url": "",
    "source": "CrossRef",
    "direction": "DigiEnergy",
    "subcategory": "Load Forecasting & Demand Management",
    "direction_label": "AI & Data Science for Urban Energy Systems",
    "refined_category": "Optimization & Control"
  }
]