A Convolutional Sparse Periodic Transformer Network for Electric Vehicle Charging Demand Forecasting

Electric vehicle (EV) charging behavior exhibits strong spatio-temporal randomness, often leading to transient peak loads and an elevated risk of distribution network overloads. In addition, existing prediction models still face challenges in achieving high accuracy, computational efficiency, and effective modeling of multi-level periodic patterns. To address these issues, this study proposes a novel architecture termed the Convolutional Sparse Periodic Transformer Network (CSPT-Net). At the front end of the architecture, the model incorporates a one-dimensional convolutional neural network (1D-CNN) to efficiently capture local temporal features. To improve computational efficiency, the traditional global attention mechanism is replaced with a sparse attention module. Furthermore, a customized periodic time-encoding module is designed to explicitly represent multi-scale temporal regularities such as daily, weekly, and holiday cycles. Extensive experiments on a large-scale dataset containing more than 70,000 real-world charging records demonstrate that CSPT-Net achieves state-of-the-art performance across all evaluation metrics. Specifically, CSPT-Net reduces the Mean Absolute Error (MAE) to 12.21 min and enhances training efficiency by over 58% compared with the standard Transformer baseline. These results confirm that CSPT-Net effectively balances predictive accuracy and computational efficiency while demonstrating superior robustness and generalization in complex real-world environments. Consequently, the proposed framework offers a reliable and high-performance data-driven foundation for power grid load management and charging infrastructure planning.