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...,

在全球共同推进环境可持续发展的浪潮推动下，电动汽车（EVs）的保有量近期迎来爆发式增长，这也凸显了电动汽车充电预测研究的重要意义。为推动该领域的进一步研究，我们推出了UrbanEV——一款开源数据集，展示了全球电动汽车普及先锋城市中国深圳的电动汽车充电泊位可用情况与电力消耗量。UrbanEV包含了丰富的充电数据资源，涵盖充电占用情况、充电时长、充电量与充电价格等维度，以每小时为采样间隔，覆盖了超过2万个独立充电站的六个月时长数据。除上述核心属性外，该数据集还囊括了天气状况、空间邻近性等多种影响因素。研究团队已通过定性与定量相结合的方式对这些因素展开全面分析，以揭示其与电动汽车充电行为之间的相关性及因果影响。此外，团队已开展了全面的实验以验证……（原文截断）。为构建全面可靠的基准数据集，我们从数据采集到数据集评估全程采用了一系列严谨的流程。整体工作流依次包含数据获取、数据处理、统计分析与预测评估，详细说明如下。

## 研究区域与数据采集
作为全球电动汽车普及的先锋城市，深圳被选为本次研究的区域，旨在为电动汽车产业发展提供极具价值的见解，以供其他城市参考。本次研究覆盖深圳市全域，对全市分布的公共电动汽车充电站数据进行了细致采集。具体而言，电动汽车充电数据从面向电动汽车驾驶员的公共充电站定位移动平台自动获取。通过该平台，用户可查询每台充电桩的实时信息，包括其可用……（原文截断）。

# UrbanEV：一款面向城市电动汽车充电需求预测的开源基准数据集

## 数据说明
UrbanEV数据集的开发旨在满足城市环境中电动汽车充电需求理解与预测的迫切需求。随着全球电动汽车普及率不断提升，高效的充电基础设施管理对于保障电网稳定性、优化用户体验至关重要。该数据集采集自中国深圳（全球电动汽车普及领先城市）的公共电动汽车充电站，覆盖2022年9月1日至2023年2月28日共计六个月的时长，能够捕捉充电模式的季节变化特征。为保障数据质量，原始记录经过了细致的预处理流程，包括提取关键信息（可用状态、额定功率与充电费用）、剔除异常值，并通过前后向填充法填补缺失值。针对四分位距（IQR）法识别出的异常值，我们将其替换为相邻的有效数值。数据经聚合处理……（原文截断）。