Short-Term EV Charging Load Predicting Based on Adaptive VMD and LSTM Methods

作者：Quanxue Guan, Qinhe Liu, Di Zhou, Yunjian Xu, Xiaojun Tan · 年份：2023 · DOI：10.1109/iecon51785.2023.10312686 · 被引用次数：9 · 研究领域：Advanced Battery Technologies Research、Electric Vehicles and Infrastructure、Electric and Hybrid Vehicle Technologies

The uncoordinated charging of large-scale electric vehicles (EVs) generally deteriorates the peak-valley difference of daily electric demands. To facilitate the operation of charging stations and electric power distributers, this work proposes a charging load prediction algorithm by combining the Variational Mode Decomposition (VMD) and the Long Short-Term Memory (LSTM) methods. The VMD is adopted to extract the EV charging load features at different time scales, obtaining multiple intrinsic mode functions (IMFs). Then the LSTM establishes the dependencies between these IMFs of historical data and the predicted load. To trade-off between the prediction accuracy and the computation overhead, an additional Snake Optimization (SO) technique is applied to adaptively optimize the VMD parameters. Experimental results show that the proposed algorithm outperforms the traditional LSTM alone and the Gate Recurrent Unit alone neural networks in terms of the overall prediction accuracy. The proposed parallel LSTM structure with the optimized VMD further reduces the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE) significantly by 55.1% and 55.9% with respect to the LSTM method with non-optimized VMD.