Short-Term Load Forecasting of Electric Vehicle Charging Stations Accounting for Multifactor IDBO Hybrid Models

Minan Tang (), Changyou Wang, Jiandong Qiu, Hanting Li, Xi Guo and Wenxin Sheng
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Minan Tang: College of New Energy and Power Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
Changyou Wang: College of New Energy and Power Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
Jiandong Qiu: College of Electrical and Mechanical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
Hanting Li: College of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
Xi Guo: College of New Energy and Power Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
Wenxin Sheng: College of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China

Energies, 2024, vol. 17, issue 12, 1-19

Abstract: The charging behavior of electric vehicle users is highly stochastic, which makes the short-term prediction of charging load at electric vehicle charging stations difficult. In this paper, a data-driven hybrid model optimized by the improved dung beetle optimization algorithm (IDBO) is proposed to address the problem of the low accuracy of short-term prediction. Firstly, the charging station data are preprocessed to obtain clear and organized load data, and the input feature matrix is constructed using factors such as temperature, date type, and holidays. Secondly, the optimal CNN-BiLSTM model is constructed using convolutional neural network (CNN) and Bi-directional Long Short-Term Memory (BiLSTM), which realizes the feature extraction of the input matrix and better captures the hidden patterns and regularities in it. Then, methods such as Bernoulli mapping are used to improve the DBO algorithm and its hyperparameters; for example, hidden neurons of the hybrid model are tuned to further improve the model prediction accuracy. Finally, a simulation experiment platform is established based on MATLAB R2023a to validate the example calculations on the historical data of EV charging stations in the public dataset of ANN-DATA, and comparative analyses are carried out. The results show that compared with the traditional models such as CNN, BiLSTM and PSO-CNN-BiLSTM, the coefficient of determination of the model exceeds 0.8921 and the root mean square error is maintained at about 4.413 on both the training and test sets, which proves its effectiveness and stability.

Keywords: data driven; electric vehicle; charging load; convolutional neural network; gated recurrent neural network; dung beetle optimization algorithm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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