EV load forecasting using a refined CNN-LSTM-AM

Electric vehicle (EV) load forecasting is becoming increasingly important for power system operation. Accurately multi-step-ahead forecasting EV loads is challenging. The correlation between the series at different time intervals and the key points in forecasting the time series will affect the results of EV load forecasting. Therefore, in this paper, a method is presented for the combination of time series of different length intervals into a hybrid CNN-LSTM-AM model for multi-step-ahead forecasting. The input matrix consists of combining time series of different lengths. A designed CNN network with a one-dimensional convolutional structure is used to extract features. After the convolutional layer, the temporal features remain. Finally, LSTM Encoder-Decoder and Attention Mechanism (AM) are combined to solve the problem of forgetting multi-step-ahead forecasting. Through the validation of the public ACN-data, it is demonstrated that the proposed method achieve accurate prediction results. According to error metrics, MAE, RMSE and R² outperform other models with a value of 0.5268, 0.9519 and 0.9138 respectively. The maximum number of multi-step-ahead prediction reaches 96 steps. This provides a reference for longer multi-step predictions in the future. It is also confirmed in the ACN-data that the accuracy of the hybrid model is better than the single model in EV load prediction.