Transfer learning based hybrid model for power demand prediction of large-scale electric vehicles

Chenlu Tian, Yechun Liu, Guiqing Zhang, Yalong Yang, Yi Yan and Chengdong Li

Energy, 2024, vol. 300, issue C

Abstract: Accurately predicting the power demand of large-scale electric vehicles (EVs) is one of the key tasks of power grid operation optimization. However, this task is difficult to complete due to insufficient data and high randomness of power demand. To address this issue, this paper proposes a transfer learning based hybrid method for power demand prediction of large-scale EVs. Firstly, the linear trend of power demand is extracted by Multiple Linear Regression (MLR), and the nonlinear residual error is obtained by removing the trend from the original data. Secondly, the residual error is predicted via the bidirectional long short-term memory network (BiLSTM). Meanwhile, transfer learning is employed to improve the prediction accuracy of BiLSTM. The BiLSTM is pre-trained using the residual error data from building energy consumption and fine-tuned by the residual error data from EV power demand. Finally, the extracted trend of large-scale EVs’ power demand and the predicted residual errors are combined to obtain the final predicted results. To validate the proposed method, the power demand of a real EV charging station is predicted using the proposed method, and fifteen models are taken for comparison (including the most popular data-driven models). The experimental result indicates that the proposed method can improve the predictive performance by at least 14.23% compared with the comparative models in terms of symmetric mean absolute percentage error, effectively avoids negative transfer results, and significantly enhance the confidence level.

Keywords: Deep learning; Large-scale electric vehicle; Power demand forecasting; Transfer learning (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:300:y:2024:i:c:s0360544224012349

DOI: 10.1016/j.energy.2024.131461

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