Deep-learning-for-electric-vehicle-charging-load-forecasting

The forecasting of charging power load for Electric Vehicles (EVs) has become crucial for ensuring grid stability and efficiency. In this study, the forecasting capabilities of Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and a novel Residual Seq2Seq model are evaluated. Additionally, Principal Component Analysis (PCA) is employed to enhance model performance through dimensionality reduction. The traditional Seq2Seq architecture is extended by the proposed Residual Seq2Seq model, which incorporates residual connections to better capture long-term dependencies and mitigate the vanishing gradient problem. Experimental results demonstrate the efficacy of this novel approach in accurately predicting the intricate temporal patterns inherent in EV charging load data. Furthermore, significant improvements in model accuracy are observed through PCA analysis when applied to the input data. The Residual Seq2Seq model, particularly when coupled with PCA-transformed data, emerges as the top performer, surpassing LSTM and BiLSTM counterparts. Through comprehensive comparative analysis, the importance of advanced neural network architectures and dimensionality reduction techniques for precise EV charging load forecasting is emphasized. These findings highlight implications for optimizing grid management and facilitating the seamless integration of electric vehicles into the smart grid infrastructure.