UrbanEV: An Open Benchmark Dataset for Urban Electric Vehicle Charging Demand Prediction DOI Creative Commons
Han Li, Haohao Qu, Xiaojun Tan

et al.

Scientific Data, Journal Year: 2025, Volume and Issue: 12(1)

Published: March 28, 2025

The recent surge in electric vehicles (EVs), driven by a collective push to enhance global environmental sustainability, has underscored the significance of exploring EV charging prediction. To catalyze further research this domain, we introduce UrbanEV - an open dataset showcasing space availability and electricity consumption pioneering city for vehicle electrification, namely Shenzhen, China. offers rich repository data (i.e., occupancy, duration, volume, price) captured at hourly intervals across extensive six-month span over 20,000 individual stations. Beyond these core attributes, also encompasses diverse influencing factors like weather conditions spatial proximity. Comprehensive experiments have been conducted showcase predictive capabilities various models, including statistical, deep learning, transformer-based approaches, using dataset. This is poised propel advancements prediction management, positioning itself as benchmark resource within burgeoning field.

Language: Английский

Citywide electric vehicle charging demand prediction approach considering urban region and dynamic influences DOI
H. H. Kuang, Kunxiang Deng, Linlin You

et al.

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 135170 - 135170

Published: March 1, 2025

Language: Английский

Citations

0
Understanding electric vehicle charging network resilience: Resilience curves and interpretable machine learning DOI
Yuwen Lu, Yan Zhang, Wei Zhai

et al.

Transportation Research Part D Transport and Environment, Journal Year: 2025, Volume and Issue: 142, P. 104709 - 104709

Published: March 21, 2025

Language: Английский

Citations

0
UrbanEV: An Open Benchmark Dataset for Urban Electric Vehicle Charging Demand Prediction DOI Creative Commons
Han Li, Haohao Qu, Xiaojun Tan

et al.

Scientific Data, Journal Year: 2025, Volume and Issue: 12(1)

Published: March 28, 2025

The recent surge in electric vehicles (EVs), driven by a collective push to enhance global environmental sustainability, has underscored the significance of exploring EV charging prediction. To catalyze further research this domain, we introduce UrbanEV - an open dataset showcasing space availability and electricity consumption pioneering city for vehicle electrification, namely Shenzhen, China. offers rich repository data (i.e., occupancy, duration, volume, price) captured at hourly intervals across extensive six-month span over 20,000 individual stations. Beyond these core attributes, also encompasses diverse influencing factors like weather conditions spatial proximity. Comprehensive experiments have been conducted showcase predictive capabilities various models, including statistical, deep learning, transformer-based approaches, using dataset. This is poised propel advancements prediction management, positioning itself as benchmark resource within burgeoning field.

Language: Английский

Citations

0