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 predictive capabilities of various models,
including statistical, deep learning, and transformer-based approaches,
using the UrbanEV dataset. This dataset is poised to propel advancements
in EV charging prediction and management, positioning itself as a
benchmark resource within this burgeoning field.

在全球共同推进环境可持续性发展的背景下，电动汽车（Electric Vehicles, EVs）保有量近期迎来爆发式增长，这一趋势凸显了电动汽车充电预测研究的重要价值。
为推动该领域的进一步研究，我们发布了UrbanEV开源数据集：该数据集采集了中国电动汽车推广先锋城市深圳的充电位可用情况与用电量数据。
UrbanEV包含丰富的充电相关数据，涵盖充电位占用率、充电时长、充电量与充电价格等维度，覆盖超2万个独立充电站，时间跨度长达6个月，采样频率为每小时一次。
除上述核心属性外，该数据集还纳入了天气状况、空间邻近性等多类影响因素。我们通过定性与定量结合的方式对这些因素展开全面分析，揭示了其对电动汽车充电行为的相关性与因果影响。
此外，我们基于UrbanEV数据集开展了全面的对比实验，验证了统计模型、深度学习模型以及基于Transformer的各类模型的预测性能。
该数据集有望推动电动汽车充电预测与管理领域的技术进步，成为该新兴领域内的基准评测资源。

搜集汇总

背景与挑战

背景概述

Urbanev数据集提供了深圳市六个月内20,000多个充电站的详细充电数据，包括充电占用率、时长、电量消耗和价格等关键信息，并整合了天气和空间数据，适用于电动汽车充电需求预测研究。