Electric vehicle charging station charging forecasting based on multi-feature extraction and multi-level transfer learning

Abstract

Electric vehicle charging station charging forecasting is crucial for charging station planning, construction, and marketing strategies in charging management platforms. However, newly built or upgraded charging stations may face problems of missing data for some time periods, insufficient historical data, and difficulties in capturing complex input features with shallow neural network models. Therefore, a charging forecasting method for electric vehicle charging stations based on multi-feature extraction and multi-level transfer learning is proposed. First, the K-Means algorithm is used to cluster users’ charging frequency over different time periods to obtain four types of charging behavioral features. These are integrated with other influencing features to form the model’s input feature set. Next, a parallel-connected multiscale hybrid temporal convolutional network (TCN) layer is designed as the feature extractor, followed by two BiLSTM layers for deeper feature learning. An Attention layer is added to strengthen individual feature selection. Finally, charging station data from source locations are classified into correlation levels, and data are fed into the model in a weak-to-strong correlation order through multi-level transfer learning. The training weights with the lowest loss function value are retained to obtain the final prediction results. Case study results show that multi-level transfer learning can compensate for the lack of data samples in new or upgraded charging stations. Compared to direct transfer learning, the proposed method reduces the mean absolute error (MAE) by 10.75%, decreases the root mean square error (RMSE) by 13.73%, and improves the R2 by 0.4%.