Multi-Encoder Spatio-Temporal Feature Fusion Network for Electric Vehicle Charging Load Prediction

Abstract

Electric vehicles (EVs) have been initiated as a preference for decarbonizing road transport. Accurate charging load prediction is essential for the construction of EV charging facilities systematically and for the coordination of EV energy demand with the requisite peak power supply. It is noted that the charging load of EVs exhibits high complexity and randomness due to temporal and spatial uncertainties. Therefore, this paper proposes a SEDformer-based charging road prediction method to capture the spatio-temporal characteristics of charging load data. As a deep learning model, SEDformer comprises multiple encoders and a single decoder. In particular, the proposed model includes a Temporal Encoder Block based on the self-attention mechanism and a Spatial Encoder Block based on the channel attention mechanism with sequence decomposition, followed by an aggregated decoder for information fusion. It is shown that the proposed method outperforms various baseline models on a real-world dataset from Palo Alto, U.S., demonstrating its superiority in addressing spatio-temporal data-driven load forecasting problems.