基于图注意力网络-门控循环单元的多源异构特征融合电动汽车充电负荷预测方法OA

To mitigate the decline in prediction accuracy caused by the limited diversity of input features and the insufficient extraction of spatiotemporal correlations in existing electric vehicle(EV)charging load forecasting models,a novel spatiotemporal forecasting framework is proposed,in which a graph attention network(GAT)is integrated with a gated recurrent unit(GRU)to effectively capture complex spatial and temporal dependencies.The model constructs a graph structure based on geographical proximity,incorporating diverse features such as historical load,weather conditions,calendar dates,and holidays.A multi-head GAT is employed to extract spatial dependencies,while the GRU models temporal dynamics.The final prediction is generated through a fully connected layer.Experimental results demonstrate that the proposed method significantly outperforms traditional methods and mainstream deep learning approaches in terms of forecasting accuracy,while also maintaining robust performance in cross-regional scenarios.This method offers data support for the dynamic scheduling of urban power grids and the orderly management of EV charging.