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2023 | OriginalPaper | Buchkapitel

Load Prediction of Electric Vehicle Charging Station Based on Residual Network

verfasst von : Renjie Wang

Erschienen in: IEIS 2022

Verlag: Springer Nature Singapore

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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.

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Titel: Load Prediction of Electric Vehicle Charging Station Based on Residual Network
verfasst von: Renjie Wang
Verlag: Springer Nature Singapore
Buch: IEIS 2022
Print ISBN: 978-981-9936-17-5

Electronic ISBN: 978-981-9936-18-2

Copyright-Jahr: 2023
DOI: https://doi.org/10.1007/978-981-99-3618-2_13