中国电机工程学报2025，Vol.45Issue(4)：1424-1435,中插16,13.DOI:10.13334/j.0258-8013.pcsee.231589
计及多公共充电站差异化耦合关联的电动汽车充电负荷时-空短期预测
Short-term Spatial-temporal Forecasting of Electric Vehicle Charging Load With Differentiated Spatial-temporal Coupling Correlation of Multiple Public Charging Stations
摘要
Abstract
The existing EV charging load forecasting studies are mostly conducted on a single forecasting object.Meanwhile,less research has been conducted on the spatial-temporal forecasting of charging loads at multiple public charging stations.The charging loads of public charging stations fluctuate drastically and are more difficult to forecast compared to those at private charging facilities.To this end,an adaptive spatial-temporal graph neural convolutional network based spatial-temporal short-term forecasting method for charging loads at multi-public charging stations is proposed.First,multi-node feature sets are constructed by using Rapid-MIC.Through data adaptive graph generation,a similar-weighted spatial-temporal graph is constructed to reconstruct the spatial connection relationship of multi-public charging stations.Then,graph convolution layers are constructed to generate the spatial aggregation features based on the differentiated coupled spatial-temporal correlations of each node,so as to realize the differential feature enhancement of nodes in the whole domain.Meanwhile,the charging patterns of different nodes are learned by node adaptive parameter learning.Finally,the temporal domain features of spatial aggregation features are mined by gated recurrent unit layers.The symmetric mean absolute percentage error(SMAPE)and mean absolute error(MAE)values of the proposed spatial-temporal forecasting method for charging loads at public charging stations are 12.95%and 31.72 kW.关键词
充电负荷时-空短期预测/多公共充电站/图神经网络/自适应图生成/差异化时空耦合关联/节点自适应参数学习Key words
spatial-temporal short-term charging load forecasting/multi-public charging stations/graph neural network/adaptive graph generation/differentiated spatial-temporal coupling correlation/node adaptive parameter learning分类
动力与电气工程引用本文复制引用
黄南天,孙赫宏,王圣元,蔡国伟,张良,王日俊..计及多公共充电站差异化耦合关联的电动汽车充电负荷时-空短期预测[J].中国电机工程学报,2025,45(4):1424-1435,中插16,13.基金项目
吉林省科技发展计划项目(20210201126GX) （20210201126GX）
国家重点研发计划项目(2022YFB2404002). Science and Technology Development Plan of Jilin Province(20210201126GX) （2022YFB2404002）
National Key R&D Program of China(2022YFB2404002). （2022YFB2404002）
