Multi-Scale Spatio-Temporal Graph Transformer with xLSTM for Electric Vehicle Charging Demand Prediction

Abstract

Accurate prediction of electric vehicle (EV) charging demand is critical for planning charging infrastructure and allocating resources efficiently. However, existing methods often fail to capture multi-scale spatial dependencies and struggle to model both long-range temporal dependencies and short-term fluctuations. To address these limitations, a deep learning framework, STGFormer, is developed for citywide EV charging demand forecasting. First, a temporal dilated convolution module (TDConv) is proposed to extract multi-scale local temporal patterns. Second, an adaptive spatial dilated graph attention module (ADGAT) is proposed to mine multi-hop spatial correlations between geographically adjacent and functionally similar regions. Third, a hybrid xLSTM-Transformer encoder captures global temporal dependencies while preserving local continuity. The performance of STGFormer is evaluated on a real-world dataset from Shenzhen. Extensive experiments demonstrate that STGFormer consistently outperforms fifteen representative baseline models. It achieves average improvements of 8.58% in RMSE, 13.95% in RAE, and 14.64% in MAE.