Load Prediction of Electric Vehicle Charging Station Based on Residual Network

Renjie Wang ()
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Renjie Wang: Beijingjiaotong University

A chapter in IEIS 2022, 2023, pp 132-143 from Springer

Abstract: 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.

Keywords: charging load; prediction; residual network (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnopch:978-981-99-3618-2_13

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DOI: 10.1007/978-981-99-3618-2_13

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