  The rapid expansion of electric vehicles (EVs) has rendered the load
forecasting of electric vehicle charging stations (EVCS) increasingly critical.
The primary challenge in achieving precise load forecasting for EVCS lies in
accounting for the nonlinear of charging behaviors, the spatial interactions
among different stations, and the intricate temporal variations in usage
patterns. To address these challenges, we propose a Multiscale Spatio-Temporal
Enhanced Model (MSTEM) for effective load forecasting at EVCS. MSTEM
incorporates a multiscale graph neural network to discern hierarchical
nonlinear temporal dependencies across various time scales. Besides, it also
integrates a recurrent learning component and a residual fusion mechanism,
enhancing its capability to accurately capture spatial and temporal variations
in charging patterns. The effectiveness of the proposed MSTEM has been
validated through comparative analysis with six baseline models using three
evaluation metrics. The case studies utilize real-world datasets for both fast
and slow charging loads at EVCS in Perth, UK. The experimental results
demonstrate the superiority of MSTEM in short-term continuous load forecasting
for EVCS.
