Multi-scale fusion transformer for EV charging station load prediction

Electric vehicle (EV) charging station load prediction is crucial for ensuring the stable operation of power grids and optimizing charging infrastructure. However, the stochastic nature of charging behaviors and the complex influence of external factors pose significant challenges to accurate prediction. To address these issues, this study proposes a novel Transformer-based architecture, the Multi-scale Fusion Transformer (MFT), which integrates a Multi-scale Modeling Mechanism (3M), a Feature-correlation Analysis Module (FAM), and a Multi-variable Fusion Module (MFM). The 3M enhances the model's ability to capture temporal dependency across varying granularities, while the FAM identifies key external features such as weather and traffic patterns. The MFM dynamically fuses these features based on their relevance to each sample using a cross-attention mechanism. Experimental evaluations using real-world data from Norway demonstrate that MFT significantly outperforms baseline models in both short-term and long-term forecasting horizons. Notably, MFT exhibits superior stability and accuracy, especially in long-term prediction tasks, with up to 25.59% average performance improvement over competitors. These results confirm the effectiveness of MFT in modeling complex, multi-scale, and externally influenced load patterns, offering a robust solution for intelligent grid scheduling and energy resource management in EV-dominated futures.