Load Prediction of Electric Vehicle Charging Station Based on Residual Network

The chapter delves into the critical issue of predicting electric vehicle (EV) charging station loads, driven by the rapid growth of EVs and the need for efficient charging infrastructure. By leveraging a residual neural network model, the study captures both spatial and temporal distribution characteristics of charging loads, addressing the challenges posed by volatile load fluctuations. The proposed model, inspired by Spatio-Temporal Residual Networks (St-ResNet), incorporates a ResPlus unit designed to capture long-distance spatial dependencies, a significant advancement over previous methods. The chapter also highlights the importance of data preprocessing, transforming discrete longitude and latitude coordinates into structured charging zones and time slices, enhancing prediction accuracy. The model's performance is rigorously evaluated against baseline models, demonstrating substantial improvements in prediction accuracy. The chapter concludes by emphasizing the model's practical implications for optimizing charging station planning and management, making it a valuable resource for professionals in the field.