Urbanev: An open benchmark dataset for urban electric vehicle charging demand prediction

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 in this domain, we introduce UrbanEV—an open dataset showcasing EV charging space availability and electricity consumption in a pioneering city for vehicle electrification, namely Shenzhen, China. UrbanEV offers a rich repository of charging data (i.e., charging occupancy, duration, volume, and price) captured at hourly intervals across an extensive six-month span for over 20,000 individual charging stations. Beyond these core attributes, the dataset also encompasses diverse influencing factors like weather conditions and spatial proximity. These factors are thoroughly analyzed qualitatively and quantitatively to reveal their correlations and causal impacts on charging behaviors. Furthermore, comprehensive experiments have been conducted to showcase the pr..., To build a comprehensive and reliable benchmark dataset, we conduct a series of rigorous processes from data collection to dataset evaluation. The overall workflow sequentially includes data acquisition, data processing, statistical analysis, and prediction assessment. As follows, please see detailed descriptions.
Study area and data acquisition

Shenzhen, a pioneering city in global vehicle electrification, has been selected for this study with the objective of offering valuable insights into electric vehicle (EV) development that can serve as a reference for other urban centers. This study encompasses the entire expanse of Shenzhen, where data on public EV charging stations distributed around the city have been meticulously gathered. Specifically, EV charging data was automatically collected from a mobile platform used by EV drivers to locate public charging stations.
Through this platform, users could access real-time information on each charging pile, including its availab..., , # Urbanev: An open benchmark dataset for urban electric vehicle charging demand prediction

## Data description

The UrbanEV dataset was developed to meet the urgent need for understanding and forecasting electric vehicle (EV) charging demand in urban environments. As global EV adoption accelerates, efficient charging infrastructure management is crucial for ensuring grid stability and enhancing user experience. Collected from public EV charging stations in Shenzhen, China — a leading city in vehicle electrification — the dataset covers a six-month period (September 1, 2022, to February 28, 2023), capturing seasonal variations in charging patterns. To ensure data quality, the raw records underwent meticulous preprocessing, including the extraction of key information (availability status, rated power, and fees), anomaly removal, and missing value imputation via forward and backward filling. Outliers identified by the IQR method were replaced with adjacent valid values. The data was aggre...,