电力信息与通信技术2025，Vol.23Issue(2)：28-37,10.DOI:10.16543/j.2095-641x.electric.power.ict.2025.02.04
电动汽车充电负荷场景化分析与超短期预测方法
A Scenario-based Analysis and Ultra-short-term Forecasting Method for Electric Vehicle Charging Load
摘要
Abstract
In order to better mine the spatiotemporal characteristics of electric vehicle(EV)charging load and improve the accuracy of charging load prediction,this paper combines clustering algorithms and deep learning algorithms to propose a multi-scenario short-term forecasting method for EV charging load.Initially,based on residents'travel habits,the intrinsic distribution characteristics of EV charging data are analyzed through clustering algorithms,and different charging scenarios are constructed in conjunction with EV user behavior.Subsequently,a correlation analysis of multi-dimensional factors affecting the load is conducted to determine the optimal input combination for the prediction model.To fully capture the temporal associations of these input features,the Transformer algorithm is enhanced with long short-term memory(LSTM),and utilized to establish a charging load prediction model for each scenario.Finally,the prediction results of each scenario are integrated to obtain the overall forecast of the charging load.Experiments with actual EV charging data from Shijiazhuang city validate the effectiveness of the proposed method and confirm its superiority under different time scales and input combinations.关键词
电动汽车负荷预测/聚类分析/注意力机制/LSTM/TransformerKey words
electric vehicle load forecasting/cluster analysis/attention mechanism/LSTM/Transformer分类
信息技术与安全科学引用本文复制引用
赵伟博,范旭东,孙胜博,李闯,周颖,李德智,马笑天,郝颖..电动汽车充电负荷场景化分析与超短期预测方法[J].电力信息与通信技术,2025,23(2):28-37,10.基金项目
国家电网有限公司总部科技项目"支撑重过载台区治理的区域供用电综合预测与智能预警技术研究与应用"(5108-202218280A-2-379-XG). （5108-202218280A-2-379-XG）
