Datasets:

UEXdo
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UEX-IES-OpenDataHub

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	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, &gt;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

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UEX-IntelligentEnergySystems

A curated hub for AI-driven energy systems, smart grids, and demand-side management.

📊 Overview

This hub contains 453 curated datasets, categorized by their specific research domains.

📂 Structure

UEX-IntelligentEnergySystems_OpenDatasets.md: Human-readable database with direct links.
data.json: Machine-readable structured data.
References.bib: BibTeX citations for all included works.

🏷️ Refined Categories

Category	Count	Core Focus
Forecasting & Prediction	111	Power output, load, and weather prediction.
Optimization & Control	280	Grid stability, demand response, and scheduling.
Demand Response & IoT	31	Specialized research in this domain.
AI & Deep Learning	31	Specialized research in this domain.

🔗 Collection

This dataset is part of the UEX-IntelligentEnergySystems Collection.

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391 kB

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125 kB

Number of rows:

453

Collection including UEXdo/UEX-IES-OpenDataHub

UEX-IntelligentEnergySystems

Collection

1 item • Updated 6 days ago