LOAD PREDICTION OF ELECTRIC VEHICLE CHARGING STATION BASED ON ASC-GCN-GRU

Shang Zhongwen; Zhang Qian; Zhao Chanjuan; Yuan Weibo; Ding Jinjin

doi:10.19912/j.0254-0096.tynxb.2024-2361

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (5) : 489-497. DOI: 10.19912/j.0254-0096.tynxb.2024-2361

LOAD PREDICTION OF ELECTRIC VEHICLE CHARGING STATION BASED ON ASC-GCN-GRU

Shang Zhongwen¹, Zhang Qian¹, Zhao Chanjuan², Yuan Weibo³, Ding Jinjin³

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Abstract

For the collaborative demand of new energy power system and electric vehicle charging load, this paper proposes an attribute-augmented spatiotemporal-graph convolutional network- gated recurrent unit is proposed neural network for charging station forecasting （ASC-GCN-GRU） model to accurately predict charging station load in new energy scenarios. Firstly, in view of the limitation of the traditional method of pre-constructing a fixed adjacency matrix, the data adaptive graph generation （DAGG） module is introduced to dynamically mine the spatio-temporal correlation features between charging stations. node adaptive parameter learning （NAPL） module is used to adapt to the diversity of charging models in new energy scenarios. Secondly, considering the environmental factors closely related to new energy output, such as temperature and precipitation, as well as external factors such as date and point of interest （POI）, the node features with enhanced attribute characteristics are established by combining the temporal load of the charging station. Finally, recursive feature elimination with cross-validation （RFECV） was introduced to select the optimal collection with the best performance of ASC-GCN-GRU load forecasting model. The numerical results show that the proposed ASC-GCN-GRU model has better prediction performance than the GCN-GRU, gated recurrent unit neural network （GRU） and sparrow search algorithm-extreme learning machine （SSA-ELM）.

Key words

new energy / electric vehicles / charging stations / electric load forecasting / graph convolutional neural network / attribute enhancement feature

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Shang Zhongwen, Zhang Qian, Zhao Chanjuan, Yuan Weibo, Ding Jinjin. LOAD PREDICTION OF ELECTRIC VEHICLE CHARGING STATION BASED ON ASC-GCN-GRU[J]. Acta Energiae Solaris Sinica. 2026, 47(5): 489-497 https://doi.org/10.19912/j.0254-0096.tynxb.2024-2361

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