title stringlengths 17 173 | doi stringlengths 22 28 | url stringlengths 38 44 | journal stringclasses 13
values | year int64 2.02k 2.03k | authors stringlengths 0 91 | abstract stringlengths 0 500 | data_url stringclasses 1
value | source stringclasses 1
value | direction stringclasses 4
values | subcategory stringclasses 11
values | direction_label stringclasses 4
values | refined_category stringclasses 4
values |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
Microstructure & physicochemical properties dataset of NaCl-based salt mixtures for concentrating solar power | 10.1038/s41597-025-06437-z | https://doi.org/10.1038/s41597-025-06437-z | Scientific Data | 2,026 | Feng, Y.; Wu, Y.; Wang, W. | Abstract
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 | CrossRef | DigiEnergy | Load Forecasting & Demand Management | Novel Low/Zero Carbon Technologies | Forecasting & Prediction | |
Global 0.05° Grid-Based Dataset of Keyhole Imagery with Spatio-Temporal Indicators (1960–1984) | 10.1038/s41597-026-06866-4 | https://doi.org/10.1038/s41597-026-06866-4 | Scientific Data | 2,026 | Wang, T.; Zhang, X.; Shan, M.; Deng, M.; Wang, J. | Abstract
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 | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | |
Bounding the costs of electric vehicle managed charging—supply curves for scenarios from 2025 to 2050 | 10.1038/s41597-026-07008-6 | https://doi.org/10.1038/s41597-026-07008-6 | Scientific Data | 2,026 | Matsuda-Dunn, R.; Hale, E.; Estreich, E.; Lavin, L.; Konar-Steenberg, G. | Abstract
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 | CrossRef | FLEXERGY | Electric Vehicles & Mobility | Demand Response & New Mobilities & Urban Planning | Optimization & Control | |
Co-crystal engineering unlocks high-stability perovskite solar modules | 10.1038/s41560-025-01904-8 | https://doi.org/10.1038/s41560-025-01904-8 | Nature Energy | 2,026 | CrossRef | CleanTech | Solar PV & Storage | Novel Low/Zero Carbon Technologies | Optimization & Control | |||
Negative pricing increases electricity use but challenges grid stability | 10.1038/s41560-025-01928-0 | https://doi.org/10.1038/s41560-025-01928-0 | Nature Energy | 2,026 | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | |||
The integration imperative in electricity grid transition | 10.1038/s41560-025-01915-5 | https://doi.org/10.1038/s41560-025-01915-5 | Nature Energy | 2,026 | O’Malley, M. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
Global gridded dataset of heating and cooling degree days under climate change scenarios | 10.1038/s41893-025-01754-y | https://doi.org/10.1038/s41893-025-01754-y | Nature Sustainability | 2,026 | Lizana, J.; Miranda, N.; Sparrow, S.; Wallom, D.; Khosla, R. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
Probabilistic day-ahead forecasting of system-level renewable energy and electricity demand | 10.1038/s41467-026-69015-w | https://doi.org/10.1038/s41467-026-69015-w | Nature Communications | 2,026 | Terrén-Serrano, G.; Deshmukh, R.; Martínez-Ramón, M. | Abstract
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 | CrossRef | CleanTech | Solar PV & Storage | Carbon Trading & New Business Models | Forecasting & Prediction | |
Behavioral uncertainty in EV charging drives heterogeneous grid load variability under climate goals | 10.1038/s41467-025-66796-4 | https://doi.org/10.1038/s41467-025-66796-4 | Nature Communications | 2,026 | Zhang, B.; Xin, Q.; Chen, S.; Wang, Z.; Lu, Y. | CrossRef | FLEXERGY | Electric Vehicles & Mobility | Demand Response & New Mobilities & Urban Planning | Forecasting & Prediction | ||
Reconstructing fine-scale 3D wind fields with terrain-informed machine learning | 10.1038/s41467-026-70562-5 | https://doi.org/10.1038/s41467-026-70562-5 | Nature Communications | 2,026 | Lin, C.; Tie, R.; Yi, S.; Liu, D.; Zhong, X. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | AI & Deep Learning | ||
Energy-efficient wireless sensor network for urban groundwater level monitoring using machine learning and sink mobility | 10.1038/s41598-026-39435-1 | https://doi.org/10.1038/s41598-026-39435-1 | Scientific Reports | 2,026 | Manchanda, R.; Lakshmi, A.; Kaur, G.; Sudhamsu, G.; Samal, S. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | AI & Deep Learning | ||
Predicting energy prices and renewable energy adoption through an optimized tree-based learning framework with explainable artificial intelligence | 10.1038/s41598-026-35706-z | https://doi.org/10.1038/s41598-026-35706-z | Scientific Reports | 2,026 | Tang, T. | Abstract
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 | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | AI & Deep Learning | |
Innovative fuzzy reinforcement learning based energy management for smart homes through optimization of renewable energy resources with starfish optimization algorithm | 10.1038/s41598-026-40247-6 | https://doi.org/10.1038/s41598-026-40247-6 | Scientific Reports | 2,026 | Hamedani, M.; Jahangiri, A.; Mehri, R.; Shamim, A. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | Demand Response & New Mobilities & Urban Planning | Optimization & Control | ||
Comparative evaluation of several models for forecasting hourly electricity use in a steel plant | 10.1038/s41598-026-43868-z | https://doi.org/10.1038/s41598-026-43868-z | Scientific Reports | 2,026 | Gu, F.; Zhao, Y. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Forecasting & Prediction | ||
A collaborative multi-party encryption for mitigating man-in-the-middle attacks in smart grid and energy IoT systems | 10.1038/s41598-026-43856-3 | https://doi.org/10.1038/s41598-026-43856-3 | Scientific Reports | 2,026 | Alfawair, M. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
The peak shifting electricity consumption management and influencing factors of smart grid from recurrent neural network model and deep learning | 10.1038/s41598-026-35754-5 | https://doi.org/10.1038/s41598-026-35754-5 | Scientific Reports | 2,026 | Wang, F.; Huang, D.; Lu, W. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
Quantum-driven frequency stability in Indian prospect smart grid with electric vehicle charging station integration and real-time hardware validation | 10.1038/s41598-025-32156-x | https://doi.org/10.1038/s41598-025-32156-x | Scientific Reports | 2,026 | Kaleeswari, M.; Sivakumar, P.; Aswini, A. | CrossRef | FLEXERGY | Electric Vehicles & Mobility | Demand Response & New Mobilities & Urban Planning | Optimization & Control | ||
A multi strategy optimization framework using AI digital twins for smart grid carbon emission reduction | 10.1038/s41598-026-38720-3 | https://doi.org/10.1038/s41598-026-38720-3 | Scientific Reports | 2,026 | Sakthivel, S.; Arivukarasi, M.; Charulatha, G.; Nithisha, J.; Abirami, B. | Abstract
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 | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Forecasting & Prediction | |
Electric vehicle charging station recommendation system based on graph neural network and context-aware refinement | 10.1038/s41598-026-41271-2 | https://doi.org/10.1038/s41598-026-41271-2 | Scientific Reports | 2,026 | Seo, D.; Moon, J.; Kwon, H. | CrossRef | FLEXERGY | Electric Vehicles & Mobility | Demand Response & New Mobilities & Urban Planning | AI & Deep Learning | ||
A multi-dimensional feature aggregation network for electric vehicle charging demand prediction | 10.1038/s41598-026-38855-3 | https://doi.org/10.1038/s41598-026-38855-3 | Scientific Reports | 2,026 | Yu, Y.; He, L.; Yu, Z.; Tu, Y.; Jing, X. | Abstract
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 | CrossRef | FLEXERGY | Electric Vehicles & Mobility | Demand Response & New Mobilities & Urban Planning | Forecasting & Prediction | |
Optimized scheduling of integrated energy systems considering waste-to-power plants and advanced adiabatic air compression energy storage machines | 10.1038/s41598-026-37485-z | https://doi.org/10.1038/s41598-026-37485-z | Scientific Reports | 2,026 | Wang, W.; Liu, M.; Zhao, H.; Wu, Y.; Tian, Y. | Abstract
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 | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | |
Energy consumption forecasting in logistics considering environmental and operational constraints using FT-transformer architecture | 10.1038/s41598-025-34414-4 | https://doi.org/10.1038/s41598-025-34414-4 | Scientific Reports | 2,026 | Yan, L. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Forecasting & Prediction | ||
Minimization of outage probability and energy consumption by deep learning-based prediction in D2D mm wave communication | 10.1038/s41598-025-34846-y | https://doi.org/10.1038/s41598-025-34846-y | Scientific Reports | 2,026 | Bilal, N.; Velmurugan, T. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Forecasting & Prediction | ||
A probabilistic framework for effective battery energy storage sizing in microgrids with demand response | 10.1038/s41598-026-35145-w | https://doi.org/10.1038/s41598-026-35145-w | Scientific Reports | 2,026 | Alamir, N.; Kamel, S.; Megahed, T.; Hori, M.; Abdelkader, S. | CrossRef | FLEXERGY | Demand Response | Demand Response & New Mobilities & Urban Planning | Optimization & Control | ||
Optimized economic scheduling of demand response in integrated energy systems considering dynamic energy efficiency and dynamic carbon trading | 10.1038/s41598-025-33497-3 | https://doi.org/10.1038/s41598-025-33497-3 | Scientific Reports | 2,026 | Mao, H.; Deng, Q.; Zhang, Z.; Yang, X. | CrossRef | EnergiTrade | Energy & Carbon Trading | Demand Response & New Mobilities & Urban Planning | Optimization & Control | ||
Renewable-powered high-temperature compressed air energy storage to accelerate grid decarbonization | 10.1016/j.crsus.2026.100639 | https://doi.org/10.1016/j.crsus.2026.100639 | Cell Reports Sustainability | 2,026 | Yang, D.; Wang, J.; Tang, G.; He, W. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
Inventory Optimization under Tri Phased Demand with Dual Aging and Controlled Backlogging | 10.1016/j.isci.2026.115268 | https://doi.org/10.1016/j.isci.2026.115268 | iScience | 2,026 | E, A.; S, U. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
Electroluminescent perovskite QD–based neural networks for energy-efficient and accelerate multitasking learning | 10.1126/sciadv.ady8518 | https://doi.org/10.1126/sciadv.ady8518 | Science Advances | 2,026 | Park, Y.; Wang, G. |
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
1−
x
FA
| CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | AI & Deep Learning | |
On-demand cancer immunotherapy via single-cell encapsulation of synthetic circuit–engineered cells | 10.1126/sciadv.aea3573 | https://doi.org/10.1126/sciadv.aea3573 | Science Advances | 2,026 | Zhao, Y.; Li, R.; Han, Y.; Shi, C.; Lee, K. |
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 | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | |
Programmable electric tweezers | 10.1126/sciadv.aec3443 | https://doi.org/10.1126/sciadv.aec3443 | Science Advances | 2,026 | Chen, Y.; Tan, H.; Zhuang, J.; Xu, Y.; Zhang, C. | 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 | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | |
UrbanEV: An Open Benchmark Dataset for Urban Electric Vehicle Charging Demand Prediction | 10.1038/s41597-025-04874-4 | https://doi.org/10.1038/s41597-025-04874-4 | Scientific Data | 2,025 | Li, H.; Qu, H.; Tan, X.; You, L.; Zhu, R. | CrossRef | FLEXERGY | Electric Vehicles & Mobility | Demand Response & New Mobilities & Urban Planning | Forecasting & Prediction | ||
CPVPD-2024: A Chinese photovoltaic plant dataset derived via a topography-enhanced deep learning framework | 10.1038/s41597-025-05891-z | https://doi.org/10.1038/s41597-025-05891-z | Scientific Data | 2,025 | Yang, Y.; Lin, S.; Lu, R.; Liu, X. | CrossRef | CleanTech | Solar PV & Storage | Novel Low/Zero Carbon Technologies | AI & Deep Learning | ||
Longitudinal Dataset of Net-load, PV Production and Solar Irradiation from Madeira Island, Portugal | 10.1038/s41597-025-06118-x | https://doi.org/10.1038/s41597-025-06118-x | Scientific Data | 2,025 | Pereira, L.; Monteiro, D.; Apina, F.; Scuri, S.; Barreto, M. | Abstract
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 | CrossRef | CleanTech | Solar PV & Storage | Novel Low/Zero Carbon Technologies | Forecasting & Prediction | |
A Real-World Energy Management Dataset from a Smart Company Building for Optimization and Machine Learning | 10.1038/s41597-025-05186-3 | https://doi.org/10.1038/s41597-025-05186-3 | Scientific Data | 2,025 | Engel, J.; Castellani, A.; Wollstadt, P.; Lanfermann, F.; Schmitt, T. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
China’s product-level CO2 emissions dataset aligned with national input-output tables from 1997 to 2020 | 10.1038/s41597-025-04366-5 | https://doi.org/10.1038/s41597-025-04366-5 | Scientific Data | 2,025 | Li, X.; Liu, Y.; Zhang, J.; Zhou, M.; Meng, B. | 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 | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Demand Response & IoT | |
A hierarchical dataset on multiple energy consumption and PV generation with emissions and weather information | 10.1038/s41597-025-06010-8 | https://doi.org/10.1038/s41597-025-06010-8 | Scientific Data | 2,025 | Dong, H.; Zhu, J.; Chung, C.; Liang, Z.; Yang, H. | Abstract
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 | CrossRef | CleanTech | Solar PV & Storage | Novel Low/Zero Carbon Technologies | Forecasting & Prediction | |
COFACTOR Drammen dataset - 4 years of hourly energy use data from 45 public buildings in Drammen, Norway | 10.1038/s41597-025-04708-3 | https://doi.org/10.1038/s41597-025-04708-3 | Scientific Data | 2,025 | Lien, S.; Walnum, H.; Sørensen, Å. | Abstract
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 | CrossRef | DigiEnergy | Load Forecasting & Demand Management | Demand Response & New Mobilities & Urban Planning | Forecasting & Prediction | |
5G High Density Demand Dataset in Liverpool City Region, UK | 10.1038/s41597-025-06282-0 | https://doi.org/10.1038/s41597-025-06282-0 | Scientific Data | 2,025 | Maheshwari, M.; Raschellà, A.; Mackay, M.; Eiza, M.; Wetherall, J. | Abstract
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 | CrossRef | DigiEnergy | Load Forecasting & Demand Management | Carbon Trading & New Business Models | Forecasting & Prediction | |
A 20-year dataset (2001–2020) of global cropland water-use efficiency at 1-km grid resolution | 10.1038/s41597-025-04904-1 | https://doi.org/10.1038/s41597-025-04904-1 | Scientific Data | 2,025 | Jiang, M.; Zheng, C.; Jia, L.; Chen, J. | Abstract
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 | CrossRef | DigiEnergy | Load Forecasting & Demand Management | Carbon Trading & New Business Models | Optimization & Control | |
Underground well water level observation grid dataset from 2005 to 2022 | 10.1038/s41597-025-04799-y | https://doi.org/10.1038/s41597-025-04799-y | Scientific Data | 2,025 | Wang, M.; Yao, J.; Chang, H.; Liu, R.; Xu, N. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
Dataset of CO2 geological storage potential and injection rate capacity in China based on fine grid technology | 10.1038/s41597-025-04875-3 | https://doi.org/10.1038/s41597-025-04875-3 | Scientific Data | 2,025 | Fan, J.; Xiang, X.; Yao, Y.; Li, K.; Li, Z. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
HIPGDAC-ES: historical population grid data compilation for Spain (1900–2021) | 10.1038/s41597-025-04533-8 | https://doi.org/10.1038/s41597-025-04533-8 | Scientific Data | 2,025 | Goerlich, F. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | Novel Low/Zero Carbon Technologies | Optimization & Control | ||
High-temporal-resolution dataset of uni-, bidirectional, and dynamic electric vehicle charging profiles | 10.1038/s41597-025-05524-5 | https://doi.org/10.1038/s41597-025-05524-5 | Scientific Data | 2,025 | Esser, M.; Orfanoudakis, S.; Homaee, O.; Vahidinasab, V.; Vergara, P. | Abstract
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 | CrossRef | FLEXERGY | Electric Vehicles & Mobility | Demand Response & New Mobilities & Urban Planning | Optimization & Control | |
Unveiling Energy Dynamics of Battery Electric Vehicle Using High-Resolution Data | 10.1038/s41597-025-06148-5 | https://doi.org/10.1038/s41597-025-06148-5 | Scientific Data | 2,025 | Yasko, M.; Moussa Issaka, A.; Tian, F.; Kazmi, H.; Driesen, J. | Abstract
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 | CrossRef | FLEXERGY | Electric Vehicles & Mobility | Demand Response & New Mobilities & Urban Planning | Optimization & Control | |
High-resolution gridded dataset of China’s offshore wind potential and costs under technical change | 10.1038/s41597-025-04428-8 | https://doi.org/10.1038/s41597-025-04428-8 | Scientific Data | 2,025 | An, K.; Cai, W.; Lu, X.; Wang, C. | CrossRef | DigiEnergy | Renewable Energy Resource Mapping | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
Power price stability and the insurance value of renewable technologies | 10.1038/s41560-025-01704-0 | https://doi.org/10.1038/s41560-025-01704-0 | Nature Energy | 2,025 | Navia Simon, D.; Diaz Anadon, L. | Abstract
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 | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Forecasting & Prediction | |
Grid-scale corrosion-free Zn/Br flow batteries enabled by a multi-electron transfer reaction | 10.1038/s41560-025-01907-5 | https://doi.org/10.1038/s41560-025-01907-5 | Nature Energy | 2,025 | Xu, Y.; Li, T.; Peng, Z.; Xie, C.; Li, X. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
AI data centres as grid-interactive assets | 10.1038/s41560-025-01927-1 | https://doi.org/10.1038/s41560-025-01927-1 | Nature Energy | 2,025 | Colangelo, P.; Coskun, A.; Megrue, J.; Roberts, C.; Sengupta, S. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | Carbon Trading & New Business Models | Optimization & Control | ||
Spatiotemporal assessment of renewable adequacy during diverse extreme weather events in China | 10.1038/s41467-025-60264-9 | https://doi.org/10.1038/s41467-025-60264-9 | Nature Communications | 2,025 | Jiang, K.; Liu, N.; Wang, K.; Chen, Y.; Wang, J. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Forecasting & Prediction | ||
Efficiency optimization for large-scale droplet-based electricity generator arrays with integrated microsupercapacitor arrays | 10.1038/s41467-025-64289-y | https://doi.org/10.1038/s41467-025-64289-y | Nature Communications | 2,025 | Li, Z.; Chen, S.; Fu, Y.; Li, J. | Abstract
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 | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | |
Speed modulations in grid cell information geometry | 10.1038/s41467-025-62856-x | https://doi.org/10.1038/s41467-025-62856-x | Nature Communications | 2,025 | Ye, Z.; Wessel, R. | Abstract
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 | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | |
Grid congestion stymies climate benefit from U.S. vehicle electrification | 10.1038/s41467-025-61976-8 | https://doi.org/10.1038/s41467-025-61976-8 | Nature Communications | 2,025 | Duan, C.; Motter, A. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
China’s urban EV ultra-fast charging distorts regulated price signals and elevates risk to grid stability | 10.1038/s41467-025-63199-3 | https://doi.org/10.1038/s41467-025-63199-3 | Nature Communications | 2,025 | Yu, Q.; Zhao, P.; Li, J.; Wang, H.; Yan, J. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
Entorhinal grid-like codes for visual space during memory formation | 10.1038/s41467-025-64307-z | https://doi.org/10.1038/s41467-025-64307-z | Nature Communications | 2,025 | Graichen, L.; Linder, M.; Keuter, L.; Jensen, O.; Doeller, C. | Abstract
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 | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | |
Planning the electric vehicle transition by integrating spatial information and social networks | 10.1038/s41467-025-66072-5 | https://doi.org/10.1038/s41467-025-66072-5 | Nature Communications | 2,025 | Wu, J.; Salgado, A.; González, M. | Abstract
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 | CrossRef | FLEXERGY | Electric Vehicles & Mobility | Demand Response & New Mobilities & Urban Planning | Forecasting & Prediction | |
Deep learning predicts real-world electric vehicle direct current charging profiles and durations | 10.1038/s41467-025-65970-y | https://doi.org/10.1038/s41467-025-65970-y | Nature Communications | 2,025 | Li, S.; Zhang, M.; Doel, R.; Ross, B.; Piggott, M. | Abstract
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 | CrossRef | FLEXERGY | Electric Vehicles & Mobility | Demand Response & New Mobilities & Urban Planning | Forecasting & Prediction | |
Atmospheric wind energization of ocean weather | 10.1038/s41467-025-56310-1 | https://doi.org/10.1038/s41467-025-56310-1 | Nature Communications | 2,025 | Rai, S.; Farrar, J.; Aluie, H. | Abstract
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 | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Forecasting & Prediction | |
A machine learning model for hub-height short-term wind speed prediction | 10.1038/s41467-025-58456-4 | https://doi.org/10.1038/s41467-025-58456-4 | Nature Communications | 2,025 | Zhang, Z.; Lin, L.; Gao, S.; Wang, J.; Zhao, H. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Forecasting & Prediction | ||
Trends in vertical wind velocity variability reveal cloud microphysical feedback | 10.1038/s41467-025-67541-7 | https://doi.org/10.1038/s41467-025-67541-7 | Nature Communications | 2,025 | Barahona, D.; Breen, K.; Ngo, D.; Maciel, F.; Patnaude, R. | Abstract
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 | CrossRef | DigiEnergy | Weather & Meteorological Data | AI & Data Science for Urban Energy Systems | Optimization & Control | |
Bioinspired nondissipative mechanical energy storage and release in hydrogels via hierarchical sequentially swollen stretched chains | 10.1038/s41467-025-59743-w | https://doi.org/10.1038/s41467-025-59743-w | Nature Communications | 2,025 | Savolainen, H.; Hosseiniyan, N.; Piedrahita-Bello, M.; Ikkala, O. | Abstract
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. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Forecasting & Prediction | |
Machine learning assisted composition design of high-entropy Pb-free relaxors with giant energy-storage | 10.1038/s41467-025-56443-3 | https://doi.org/10.1038/s41467-025-56443-3 | Nature Communications | 2,025 | Wang, X.; Zhang, J.; Ma, X.; Luo, H.; Liu, L. | Abstract
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 | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | AI & Deep Learning | |
Unlocking global carbon reduction potential by embracing low-carbon lifestyles | 10.1038/s41467-025-59269-1 | https://doi.org/10.1038/s41467-025-59269-1 | Nature Communications | 2,025 | Guan, Y.; Shan, Y.; Hang, Y.; Nie, Q.; Liu, Y. | Abstract
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, | CrossRef | DigiEnergy | Load Forecasting & Demand Management | Carbon Trading & New Business Models | Demand Response & IoT | |
Dynamic grid management reduces wildfire adaptation costs in the electric power sector | 10.1038/s41558-025-02436-5 | https://doi.org/10.1038/s41558-025-02436-5 | Nature Climate Change | 2,025 | Warner, C.; Callaway, D.; Fowlie, M. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
Cost-effective adaptation of electric grids | 10.1038/s41558-025-02421-y | https://doi.org/10.1038/s41558-025-02421-y | Nature Climate Change | 2,025 | Wang, Z. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
Single-fibril Förster resonance energy transfer imaging and deep learning reveal concentration dependence of amyloid β 42 aggregation pathways | 10.1093/pnasnexus/pgaf342 | https://doi.org/10.1093/pnasnexus/pgaf342 | npj Clean Energy | 2,025 | Sohail, S.; Yoo, J.; Chung, H. | Abstract
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 | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | AI & Deep Learning | |
Spatiotemporal predictions of toxic urban plumes using deep learning | 10.1093/pnasnexus/pgaf198 | https://doi.org/10.1093/pnasnexus/pgaf198 | npj Clean Energy | 2,025 | Wang, Y.; Fernández-Godino, M.; Gunawardena, N.; Lucas, D.; Yue, X. | Abstract
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 | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Forecasting & Prediction | |
Versatile phenolic composites by in situ polymerization of concentrated dispersions of carbon nanotubes | 10.1093/pnasnexus/pgaf274 | https://doi.org/10.1093/pnasnexus/pgaf274 | npj Clean Energy | 2,025 | Yu, Z.; Zhang, C.; Chen, M.; Huang, J. | Abstract
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, | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Forecasting & Prediction | |
Air-conditioning replacement to enhance the reliability of renewable power systems under extreme weather risks | 10.1093/pnasnexus/pgaf230 | https://doi.org/10.1093/pnasnexus/pgaf230 | npj Clean Energy | 2,025 | Zhu, L.; Liang, Z.; Yan, Z.; Ming, X.; Duan, H. | Abstract
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 | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Forecasting & Prediction | |
The equity implications of pecuniary externalities on an electric grid | 10.1093/pnasnexus/pgaf356 | https://doi.org/10.1093/pnasnexus/pgaf356 | npj Clean Energy | 2,025 | Sims, C.; Ali, G.; Holladay, J.; Roberson, T.; Chen, C. | Abstract
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 | CrossRef | CleanTech | Solar PV & Storage | Novel Low/Zero Carbon Technologies | Optimization & Control | |
Multicriteria models provide enhanced insight for siting US offshore wind | 10.1093/pnasnexus/pgaf051 | https://doi.org/10.1093/pnasnexus/pgaf051 | npj Clean Energy | 2,025 | Santarromana, R.; Abdulla, A.; Morgan, M.; Mendonça, J. | Abstract
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 | CrossRef | DigiEnergy | Renewable Energy Resource Mapping | AI & Data Science for Urban Energy Systems | Optimization & Control | |
Grid connections and inequitable access to electricity in African cities | 10.1038/s44284-025-00221-1 | https://doi.org/10.1038/s44284-025-00221-1 | Nature Cities | 2,025 | Kersey, J.; Massa, C.; Lukuyu, J.; Mbabazi, J.; Taneja, J. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
Control strategy evaluation for reactive power management in grid-connected photovoltaic systems under varying solar conditions | 10.1038/s41598-025-08918-y | https://doi.org/10.1038/s41598-025-08918-y | Scientific Reports | 2,025 | Adak, S. | CrossRef | CleanTech | Solar PV & Storage | Novel Low/Zero Carbon Technologies | Optimization & Control | ||
Photovoltaic solar energy prediction using the seasonal-trend decomposition layer and ASOA optimized LSTM neural network model | 10.1038/s41598-025-87625-0 | https://doi.org/10.1038/s41598-025-87625-0 | Scientific Reports | 2,025 | Mohanasundaram, V.; Rangaswamy, B. | CrossRef | CleanTech | Solar PV & Storage | Novel Low/Zero Carbon Technologies | Forecasting & Prediction | ||
Optimizing photovoltaic power plant forecasting with dynamic neural network structure refinement | 10.1038/s41598-024-80424-z | https://doi.org/10.1038/s41598-024-80424-z | Scientific Reports | 2,025 | Díaz-Bello, D.; Vargas-Salgado, C.; Alcazar-Ortega, M.; Alfonso-Solar, D. | CrossRef | CleanTech | Solar PV & Storage | Novel Low/Zero Carbon Technologies | Forecasting & Prediction | ||
Seasonal quantile forecasting of solar photovoltaic power using Q-CNN-GRU | 10.1038/s41598-025-12797-8 | https://doi.org/10.1038/s41598-025-12797-8 | Scientific Reports | 2,025 | Ait Mouloud, L.; Kheldoun, A.; Oussidhoum, S.; Alharbi, H.; Alotaibi, S. | CrossRef | CleanTech | Solar PV & Storage | Novel Low/Zero Carbon Technologies | Forecasting & Prediction | ||
An improved weighted average algorithm with Cloud-Based Risk-Conscious stochastic model for building energy optimization | 10.1038/s41598-025-30043-z | https://doi.org/10.1038/s41598-025-30043-z | Scientific Reports | 2,025 | Keawsawasvong, S.; Jearsiripongkul, T.; Khajehzadeh, M. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
Building retrofit multiobjective optimization using neural networks and genetic algorithm three for energy carbon and comfort | 10.1038/s41598-025-21871-0 | https://doi.org/10.1038/s41598-025-21871-0 | Scientific Reports | 2,025 | Duan, Z.; Li, B.; Zi, Y.; Yao, G. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
Smart building energy management with renewables and storage systems using a modified weighted mean of vectors algorithm | 10.1038/s41598-024-79782-5 | https://doi.org/10.1038/s41598-024-79782-5 | Scientific Reports | 2,025 | Ebeed, M.; hassan, S.; Kamel, S.; Nasrat, L.; Mohamed, A. | Abstract
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 | CrossRef | FLEXERGY | Smart Home & EMS | Demand Response & New Mobilities & Urban Planning | Demand Response & IoT | |
Deep reinforcement learning based low energy consumption scheduling approach design for urban electric logistics vehicle networks | 10.1038/s41598-025-92916-7 | https://doi.org/10.1038/s41598-025-92916-7 | Scientific Reports | 2,025 | Sun, P.; He, J.; Wan, J.; Guan, Y.; Liu, D. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
Spatiotemporal evolution of agricultural carbon emissions intensity in China and analysis of influencing factors | 10.1038/s41598-025-04973-7 | https://doi.org/10.1038/s41598-025-04973-7 | Scientific Reports | 2,025 | Zhu, X.; Shao, X. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Demand Response & IoT | ||
Individual perceptions of renewable energy investment in Somali firms | 10.1038/s41598-025-11581-y | https://doi.org/10.1038/s41598-025-11581-y | Scientific Reports | 2,025 | Nor, B. | Abstract
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 | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | |
Optimal energy management of multi-carrier energy system considering uncertainty in renewable generation | 10.1038/s41598-025-10404-4 | https://doi.org/10.1038/s41598-025-10404-4 | Scientific Reports | 2,025 | Garg, A.; Niazi, K.; Tiwari, S.; Sharma, S.; Rawat, T. | Abstract
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 | CrossRef | DigiEnergy | Load Forecasting & Demand Management | Novel Low/Zero Carbon Technologies | Optimization & Control | |
Optimal energy management for multi-energy microgrids using hybrid solutions to address renewable energy source uncertainty | 10.1038/s41598-025-90062-8 | https://doi.org/10.1038/s41598-025-90062-8 | Scientific Reports | 2,025 | Ramkumar, M.; Subramani, J.; Sivaramkrishnan, M.; Munimathan, A.; Michael, G. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | Carbon Trading & New Business Models | Optimization & Control | ||
Capabilities of battery and compressed air storage in the economic energy scheduling and flexibility regulation of multi-microgrids including non-renewable/renewable units | 10.1038/s41598-025-06768-2 | https://doi.org/10.1038/s41598-025-06768-2 | Scientific Reports | 2,025 | Naghibi, A.; Akbari, E.; Veisi, M.; Shahmoradi, S.; Pirouzi, S. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | Carbon Trading & New Business Models | Optimization & Control | ||
Power quality disturbance identification using hybrid deep learning in renewable energy systems | 10.1038/s41598-025-28291-0 | https://doi.org/10.1038/s41598-025-28291-0 | Scientific Reports | 2,025 | Peruman, P.; Ayyar, K. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | AI & Deep Learning | ||
Multi-criteria assessment of optimization methods for controlling renewable energy sources in distribution systems | 10.1038/s41598-025-20339-5 | https://doi.org/10.1038/s41598-025-20339-5 | Scientific Reports | 2,025 | Eid, A.; Alsafrani, A. | Abstract
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 | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | |
A bi-level optimization strategy of electricity-hydrogen-carbon integrated energy system considering photovoltaic and wind power uncertainty and demand response | 10.1038/s41598-024-84605-8 | https://doi.org/10.1038/s41598-024-84605-8 | Scientific Reports | 2,025 | Lu, M.; Teng, Y.; Chen, Z.; Song, Y. | CrossRef | CleanTech | Solar PV & Storage | Novel Low/Zero Carbon Technologies | Optimization & Control | ||
Federated two-edge graph attention network with weighted global aggregation for electricity consumption demand forecasting | 10.1038/s41598-025-28610-5 | https://doi.org/10.1038/s41598-025-28610-5 | Scientific Reports | 2,025 | Yang, M.; Ren, J.; Zeng, L.; Yang, X.; Li, S. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Forecasting & Prediction | ||
Multi-temporal dimension prediction of new energy electricity demand based on chaos-LSSVM neural network | 10.1038/s41598-025-27677-4 | https://doi.org/10.1038/s41598-025-27677-4 | Scientific Reports | 2,025 | Wu, Y.; Wang, W.; Ma, X.; Zhao, R.; Wu, B. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Forecasting & Prediction | ||
Seasonal forecasting of the hourly electricity demand applying machine and deep learning algorithms impact analysis of different factors | 10.1038/s41598-025-91878-0 | https://doi.org/10.1038/s41598-025-91878-0 | Scientific Reports | 2,025 | El-Azab, H.; Swief, R.; El-Amary, N.; Temraz, H. | Abstract
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 | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Forecasting & Prediction | |
An optimization method for integrated demand response strategies for electricity and heat considering the uncertainty of user-side loads | 10.1038/s41598-025-30090-6 | https://doi.org/10.1038/s41598-025-30090-6 | Scientific Reports | 2,025 | Li, J.; Zhang, D.; Wei, Y.; Zhou, X.; Kong, X. | CrossRef | FLEXERGY | Demand Response | Demand Response & New Mobilities & Urban Planning | Forecasting & Prediction | ||
Robust fuzzy dynamic integrated environmental-economic-social scheduling considering demand response and user’s satisfaction with electricity under multiple uncertainties | 10.1038/s41598-025-87689-y | https://doi.org/10.1038/s41598-025-87689-y | Scientific Reports | 2,025 | Zhang, H.; Xi, Q.; Chen, L.; Min, Y.; Fan, X. | CrossRef | FLEXERGY | Demand Response | Demand Response & New Mobilities & Urban Planning | Optimization & Control | ||
The electricity purchasing and selling strategy of load aggregators participating in China’s dual-tier electricity market considering inter-provincial subsidies | 10.1038/s41598-025-13385-6 | https://doi.org/10.1038/s41598-025-13385-6 | Scientific Reports | 2,025 | Zhang, H.; Tian, Y.; Liu, X.; Kuang, M.; Zhang, N. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | Carbon Trading & New Business Models | Forecasting & Prediction | ||
Systematic hyperparameter analysis of GRU and LSTM across demand pattern types: a demand-characteristic-driven meta-learning framework for rapid optimization | 10.1038/s41598-025-31508-x | https://doi.org/10.1038/s41598-025-31508-x | Scientific Reports | 2,025 | El-Meehy, A.; El-Kharbotly, A.; El-Beheiry, M. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
Time series transformer for tourism demand forecasting | 10.1038/s41598-025-15286-0 | https://doi.org/10.1038/s41598-025-15286-0 | Scientific Reports | 2,025 | Yi, S.; Chen, X.; Tang, C. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Forecasting & Prediction | ||
Demand forecasting of smart tourism integrating spatial metrology and deep learning | 10.1038/s41598-025-26830-3 | https://doi.org/10.1038/s41598-025-26830-3 | Scientific Reports | 2,025 | Ma, J. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Forecasting & Prediction | ||
Climate-adaptive energy forecasting in green buildings via attention-enhanced Seq2Seq transfer learning | 10.1038/s41598-025-16953-y | https://doi.org/10.1038/s41598-025-16953-y | Scientific Reports | 2,025 | Peng, F.; Su, T.; Zeng, Q.; Han, X. | Abstract
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 | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Forecasting & Prediction | |
Solar potential assessment using machine learning and climate change projections for long-term energy planning | 10.1038/s41598-025-23661-0 | https://doi.org/10.1038/s41598-025-23661-0 | Scientific Reports | 2,025 | Reddy, B.; Gautam, K.; Pachauri, N. | Abstract
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 | CrossRef | CleanTech | Solar PV & Storage | Novel Low/Zero Carbon Technologies | AI & Deep Learning | |
Anomaly detection with grid sentinel framework for electric vehicle charging stations in a smart grid environment | 10.1038/s41598-025-00400-z | https://doi.org/10.1038/s41598-025-00400-z | Scientific Reports | 2,025 | Kesavan, V.; Hossen, M.; Gopi, R.; Joseph, E. | CrossRef | FLEXERGY | Electric Vehicles & Mobility | Demand Response & New Mobilities & Urban Planning | Optimization & Control | ||
An online learning method for assessing smart grid stability under dynamic perturbations | 10.1038/s41598-025-94718-3 | https://doi.org/10.1038/s41598-025-94718-3 | Scientific Reports | 2,025 | Alaerjan, A.; Jabeur, R. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.