Electric Vehicle Charging Demand Forecasting Based on City Grid Attribute Classification

2021 11th International Conference on Power and Energy Systems (ICPES) Pub Date : 2021-12-18 DOI:10.1109/ICPES53652.2021.9683949

Kaiyu Zhang, Yingjie Tian, S. Shi, Yun Su, Licheng Xu, Meixia Zhang

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引用次数: 2

Abstract

In order to improve the accuracy of user travel behavior and traffic road condition description in EV charging demand prediction research, a method of EV charging demand prediction based on urban grid attribute division is proposed. Firstly, the study area is determined based on the distribution of net car trips, and then the study area is precisely divided into functional areas based on the urban point-of-interest data crawled by Python, and then the spatio-temporal characteristics of residents' trips are obtained by mining; finally, considering the charging characteristics of electric vehicles, a complete charging demand prediction model is established, and the travel behavior of electric vehicles under different spatio-temporal distributions in the Second Ring Road area of Chengdu is simulated by Monte Carlo sampling method and The simulation results show that the proposed charging demand prediction method can effectively predict the charging demand in different areas and different scenarios.

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基于城市电网属性分类的电动汽车充电需求预测

为了提高电动汽车充电需求预测研究中用户出行行为和交通路况描述的准确性，提出了一种基于城市网格属性划分的电动汽车充电需求预测方法。首先根据网约车出行分布确定研究区域，然后根据Python抓取的城市兴趣点数据对研究区域进行精确划分，再通过挖掘得到居民出行的时空特征;最后，考虑到电动汽车的充电特点，建立了完整的充电需求预测模型，并采用蒙特卡罗采样法对成都市二环区域不同时空分布下的电动汽车出行行为进行了仿真，仿真结果表明，所提出的充电需求预测方法能够有效预测不同区域、不同场景的充电需求。

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2021 11th International Conference on Power and Energy Systems (ICPES)

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