Electric Vehicle Charging Demand Prediction Model Based on Spatiotemporal Attention Mechanism

Yang Chen (), Zeyang Tang, Yibo Cui, Wei Rao and Yiwen Li
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Yang Chen: College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China
Zeyang Tang: College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China
Yibo Cui: State Grid Hubei Electric Power Research Institute, Wuhan 430077, China
Wei Rao: State Grid Hubei Electric Power Research Institute, Wuhan 430077, China
Yiwen Li: State Grid Hubei Electric Power Research Institute, Wuhan 430077, China

Energies, 2025, vol. 18, issue 3, 1-17

Abstract: The accurate estimation and prediction of charging demand are crucial for the planning of charging infrastructure, grid layout, and the efficient operation of charging networks. To address the shortcomings of existing methods in utilizing the spatial interdependencies among urban regions, this paper proposes a forecasting approach that integrates dynamic time warping (DTW) with a spatial–temporal attention graph convolutional neural network (ASTGCN). First, this method delves into the correlations between various regions within the target city, establishing intricate coupling relationships among them. Subsequently, the FastDTW algorithm is employed to construct an adjacency matrix, capturing the spatiotemporal correlation among different urban regions. Finally, the ASTGCN model is applied to predict the power load of each region, which can accurately capture the spatiotemporal characteristics of the power load. The experimental results indicate that the proposed model has a more powerful comprehensive ability to capture spatiotemporal relationships and improve accuracy and stability in different prediction steps.

Keywords: electric vehicle; charging demand; spatiotemporal distribution; FastDTW; load forecasting (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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