基于堆叠异构空间图网络的电动汽车充电负荷预测OA

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.