Charging station cluster load prediction: Spatiotemporal multi-graph fusion technology

Tuo Xie, Xinyao Yun, Gang Zhang, Hua Li, Kaoshe Zhang and Ruogu Wang

Renewable and Sustainable Energy Reviews, 2024, vol. 206, issue C

Abstract: In recent years, single-station charging load prediction technology for electric vehicles has gradually matured, but there are few prediction studies at the charging station cluster level. Therefore, this research propose a load prediction framework for electric vehicle charging station groups based on multi-graph fusion. First, a distance map, a traffic network map, and a traffic density map are established to extract the topological information of the charging station group, and the fusion operation is performed by establishing the relationship between the influencing factors based on the mutual correlation of the influencing factors and the multi-graph attention mechanism; Secondly, a spatiotemporal prediction model was constructed, multi-level feature extraction was performed, and multiple charging stations were predicted at the same time; Finally, taking the cluster load of charging stations in an urban area as an example, a comparative experiment was conducted to compare the model proposed in this study with the mathematical model, the prediction performance of different variants of machine learning models, common deep learning models and the model proposed in this study, and a comparative test with multiple prediction horizons was conducted. The research results show that the model proposed in this work improves the accuracy of multi-station forecasting and provides new ideas for data-driven charging station cluster forecasting research.

Keywords: Spatiotemporal forecasting of charging load; Attention mechanism; Graph neural network; Maximum information coefficient; Spatiotemporal feature mining; Graph convolutional neural network; Graph attention neural network; Multi-foresight prediction (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1016/j.rser.2024.114855

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