Simultaneous Multiple Charging Stations Load Forecasting with Graph Neural Network

Large-scale and stochastic electric vehicle (EV) charging behaviors pose challenges to the grid operation due to their spatial heterogeneity and temporal variability. This paper proposes a Multiple Charging Station (MCS) Collaborative Load Forecasting (MCSCLF) framework for simultaneous short-term forecasting of multiple charging stations. MCSCLF learns three latent inter-station graphs from short-term, daily and weekly charging-load views and couples them with view-specific spatiotemporal forecasting branches combining temporal convolution, graph convolution and attention features. The resulting multi-view features are fused for joint multi-station prediction. Experiments on real-world data from ten charging stations in Shenzhen demonstrate the ability of the proposed MCSCLF framework to jointly forecast station loads without a predefined spatial topology. Ablation studies show that the iterative graph refinement outperforms the tested fixed-graph and non-iterative alternatives, while the multi-scale temporal inputs and attention can further improve the forecasting accuracy within the proposed architecture.