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Electric Vehicle Charging Demand Prediction Model Based on Spatiotemporal Attention Mechanism

Author

Listed:
  • 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
    State Grid Hubei Electric Power Research Institute, Wuhan 430077, 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)

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.

Suggested Citation

  • Yang Chen & Zeyang Tang & Yibo Cui & Wei Rao & Yiwen Li, 2025. "Electric Vehicle Charging Demand Prediction Model Based on Spatiotemporal Attention Mechanism," Energies, MDPI, vol. 18(3), pages 1-17, February.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:3:p:687-:d:1582201
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    References listed on IDEAS

    as
    1. Zhu, Nanyang & Wang, Ying & Yuan, Kun & Yan, Jiahao & Li, Yaping & Zhang, Kaifeng, 2024. "GGNet: A novel graph structure for power forecasting in renewable power plants considering temporal lead-lag correlations," Applied Energy, Elsevier, vol. 364(C).
    2. Zhang, Jing & Yan, Jie & Liu, Yongqian & Zhang, Haoran & Lv, Guoliang, 2020. "Daily electric vehicle charging load profiles considering demographics of vehicle users," Applied Energy, Elsevier, vol. 274(C).
    3. Zhuang, Yingrui & Cheng, Lin & Qi, Ning & Wang, Xinyi & Chen, Yue, 2025. "Real-time hosting capacity assessment for electric vehicles: A sequential forecast-then-optimize method," Applied Energy, Elsevier, vol. 380(C).
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