Spatiotemporal Forecasting of Regional Electric Vehicles Charging Load: A Multi-Channel Attentional Graph Network Integrating Dynamic Electricity Price and Weather

Abstract

Accurate spatiotemporal forecasting of electric vehicle (EV) charging load is essential for smart grid management and efficient charging service operation. This paper introduced a novel spatiotemporal graph convolutional network with cross-attention (STGCN-Attention) for multi-factor charging load prediction. The model demonstrated a strong capability to capture cross-scale spatiotemporal correlations by adaptively integrating historical charging load, charging pile occupancy, dynamic electricity prices, and meteorological data. Evaluations in real-world charging scenarios showed that the proposed model achieved superior performance in hour forecasting, reducing Mean Absolute Error (MAE) by 9% and 16% compared to traditional STGCN and LSTM models, respectively. It also attained approximately 30% higher accuracy than 24 h prediction. Furthermore, the study identified an optimal 1-2-1 multi-scale temporal window strategy (hour–day–week) and revealed key driver factors. The combined input of load, occupancy, and electricity price yielded the best results (RMSE = 37.21, MAE = 27.34), while introducing temperature and precipitation raised errors by 2–8%, highlighting challenges in fine-grained weather integration. These findings provided actionable insights for real-time and intraday charging scheduling.