Design of smart charging station and research on load prediction of charging station based on LSTM

Liang Huang; Xiao Xiong; Zaixun Ling; Muchao Xiang

doi:10.1117/12.2646955

7 September 2022 Design of smart charging station and research on load prediction of charging station based on LSTM

Liang Huang, Xiao Xiong, Zaixun Ling, Muchao Xiang

Proceedings Volume 12329, Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022); 123290C (2022) https://doi.org/10.1117/12.2646955
Event: Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022), 2022, Changsha, China

Abstract

The electric vehicle industry has greatly developed in this years, which has led to significant growth in the number of fast-charging stations for electric vehicles. This paper takes an electric bus DC fast charging station as the research object, and proposes a smart charging station system based on the Internet of Things. It not only realizes the interconnection and intercommunication of data between the equipment and facilities of the charging station, the local control platform, and the cloud platform, but also realizes comprehensive intelligent management combining local control and remote control. According to the historical load data collected by the smart charging station, this paper also studies the performance of LSTM in the load prediction of the charging station. Build a load prediction model in MATLAB, determine the optimal network structure through simulation experiments, and select the appropriate input and output to train the model. The simulation outcomes show that the prediction accuracy of LSTM for charging station load is higher than that of traditional BP neural network, which lays a foundation for the research on energy control strategies of smart charging stations.

Citation Download Citation

Liang Huang, Xiao Xiong, Zaixun Ling, and Muchao Xiang "Design of smart charging station and research on load prediction of charging station based on LSTM", Proc. SPIE 12329, Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022), 123290C (7 September 2022); https://doi.org/10.1117/12.2646955

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