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Load Prediction of Electric Vehicle Charging Station Based on Residual Network

In: Ieis 2022

Author

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  • Renjie Wang

    (Beijingjiaotong University)

Abstract

In the context of the rapid development of electric vehicles, the uneven space-time distribution of charging station load has caused the loss of efficiency and user experience. Therefore, the space-time prediction of charging station load has become an important research problem. In this paper, based on the St-ResNet model, which has achieved excellent results in space-time flow prediction in the field of traffic flow, we establish a space-time prediction model for a load of electric vehicle charging stations. In the model, we convert the spatial features of multiple charging stations with different geographical locations into 16*16 charging areas. And then, we fuse the three temporal features of the regional spatial distribution of the charging station load, and then use ResPlus to capture the long-distance spatial dependence of the charging load. Finally, we improved 3% to 20% compared with the baseline model.

Suggested Citation

  • Renjie Wang, 2023. "Load Prediction of Electric Vehicle Charging Station Based on Residual Network," Lecture Notes in Operations Research, in: Menggang Li & Guowei Hua & Xiaowen Fu & Anqiang Huang & Dan Chang (ed.), Ieis 2022, pages 132-143, Springer.
    • Handle: RePEc:spr:lnopch:978-981-99-3618-2_13
    DOI: 10.1007/978-981-99-3618-2_13
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