Charging station load prediction based on LSTM neural network

Shuchen Tan; Lijuan Yu

doi:10.1117/12.2636461

6 May 2022 Charging station load prediction based on LSTM neural network

Shuchen Tan, Lijuan Yu

Proceedings Volume 12176, International Conference on Algorithms, Microchips and Network Applications; 121761C (2022) https://doi.org/10.1117/12.2636461
Event: International Conference on Algorithms, Microchips, and Network Applications 2022, 2022, Zhuhai, China

Abstract

The load prediction of electric vehicle charging stations is of great significance to the planning and scheduling of charging stations. In this paper, the charging load prediction of electric vehicle charging station is analyzed and studied, and the charging load of the future hour level is predicted by the short and longtime memory neural network. First of historical load data interpolation processing complete data sets, and then build LSTM neural network model of data for training, by comparing different training set and the proportion of the data set under the condition of MSE is worth out of prediction scheme, escape the cycle of neural network (RNN) step after a long time the problem of dissipate, intuitive experimental results.

Citation Download Citation

Shuchen Tan and Lijuan Yu "Charging station load prediction based on LSTM neural network", Proc. SPIE 12176, International Conference on Algorithms, Microchips and Network Applications, 121761C (6 May 2022); https://doi.org/10.1117/12.2636461

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