电力系统自动化2024，Vol.48Issue(7)：86-93,8.DOI:10.7500/AEPS20230611001
基于动态自适应图神经网络的电动汽车充电负荷预测
Electric Vehicle Charging Load Prediction Based on Dynamic Adaptive Graph Neural Network
摘要
Abstract
The uncertainty and long-term prediction of the load fluctuation of electric vehicle(EV)charging stations pose significant challenges to accurately predict the charging load.An EV charging load prediction based on dynamic adaptive graph neural network is proposed.Firstly,a spatiotemporal correlation feature extraction layer for charging load information is constructed.By combining multi-head attention mechanism with adaptive relevance graph,a comprehensive feature representation with spatiotemporal correlation is generated to capture the load fluctuation of EV charging station.Then,the extracted features are input into a spatiotemporal convolutional layer to capture the coupling relationship between time and space.The ability of the model to couple long time series is enhanced by Chebyshev polynomial graph convolution and multi-scale temporal convolution.The effectiveness of the algorithm has been verified using two real datasets.Taking the Palo Alto dataset as an example,compared with existing methods,the average prediction error of this algorithm under 4 volatile conditions is reduced sharply.关键词
电动汽车/负荷预测/时空关联特征/自适应图神经网络/注意力机制/时空卷积层Key words
electric vehicle/load prediction/spatiotemporal correlation feature/adaptive graph neural network/attention mechanism/spatiotemporal convolutional layer引用本文复制引用
张延宇,张智铭,刘春阳,张西镚,周毅..基于动态自适应图神经网络的电动汽车充电负荷预测[J].电力系统自动化,2024,48(7):86-93,8.基金项目
国家自然科学基金资助项目(62176088) （62176088）
河南省科技攻关项目(232102211034) （232102211034）
全国博士后交流计划引进项目(YJ20220262). This work is supported by National Natural Science Foundation of China(No.62176088),Program for Science&Technology Development of Henan Province(No.232102211034),and Introduction Project of National Postdoc Exchange Plan(No.YJ20220262). （YJ20220262）
