电测与仪表2026，Vol.63Issue(9)：77-86,10.DOI:10.19753/j.issn1001-1390.2026.09.008
基于堆叠异构空间图网络的电动汽车充电负荷预测
Electric vehicle charging load prediction based on stacked heterogeneous spatial graph network
摘要
Abstract
With the increasing penetration of electric vehicles,charging facilities and load will become key growth areas for urban power grid development.Accurately forecasting the spatial distribution and temporal variation of e-lectric vehicle charging load is crucial for ensuring the safe and stable operation of urban power grids.To address this,a forecasting method based on the stacked heterogeneous spatio-temporal graph network(SHSGN)model is proposed.This method improves the accuracy of electric vehicle load predictions by modeling the spatial distribu-tion patterns of urban charging loads and the diverse charging behaviors of users.A two-layer heterogeneous graph neural network model is established to represent the charging station-level and regional-level load nodes.By enhan-cing the cross-regional load transfer patterns,this model improves the ability to capture spatial load characteristics.A multi-GRU parallel decoding neural network model is developed to accurately express the differentiated charging patterns of each charging station node through independent decoding tasks.Validation using real charging load data from a Chinese city demonstrates that the proposed method reduces the charging load prediction error to 6.77%,a-chieving a 2.12%improvement over traditional methods.关键词
电动汽车/充电负荷预测/时空图神经网络/人工智能Key words
electric vehicle/charging load prediction/spatiotemporal graph neural network/artificial intelligence分类
信息技术与安全科学引用本文复制引用
李璐,王文龙,刘楠,杨威,张佳夫,王宇蛟..基于堆叠异构空间图网络的电动汽车充电负荷预测[J].电测与仪表,2026,63(9):77-86,10.基金项目
国家电网公司科技资助项目(2023YF-138) （2023YF-138）
