Charging prediction for new energy electric vehicles in the context of vehicle to grid using a hybrid ROCNN-BILSTM model

Vehicle to grid refers to the interaction between electric vehicles and the power grid through charging stations. It aims to guide owners of new energy vehicles to charge in an orderly and staggered manner, and even enabling power supply back to the grid. In the context of vehicle to grid, the charging behavior of new energy vehicles becomes different from the past due to uncertainties introduced by user plug-in/plug-out actions and weather conditions, which may disrupt owners' future scheduling plans. In this article, we propose a charging prediction study based on the Reordering Convolutional Neural Network-Bidirectional Long Short-Term Memory (ROCNN-BILSTM) hybrid model specifically designed for the vehicle to grid context. The proposed model employs wavelet threshold denoising as a data preprocessing operation to remove unnecessary noise factors that could affect predictions. Subsequently, the 2-Dimensional Convolutional Neural Network (2D-CNN) component retains temporal features while extracting spatial features. Notably, the features are rearranged, combining highly correlated ones, to facilitate the extraction of high-level, abstract spatial features by the 2D-CNN. Finally, the Bidirectional Long Short-Term Memory (BILSTM) component utilizes a bidirectional structure to capture comprehensive dynamic information and assist in achieving the final charging prediction. Our proposed ROCNN-BILSTM eliminates uncertainty in the data, allowing deep learning models to better focus on important features. Additionally, our model emphasizes high-level spatiotemporal feature extraction, which helps achieve high-performance charging prediction. In the context of vehicle to grid, a real-world dataset of new energy vehicle charging data was used for multi-step prediction, different starting point predictions, and comparison with advanced models. The experimental results show that the proposed model outperforms CNN-LSTM and 2D-CNN models by up to 50.1% and 57.1% in terms of mean absolute error (MAE), and 45.8% and 51.5% in terms of mean squared error (MSE). The results validate the strong predictive performance of the hybrid model and provide robust support for the demands of the vehicle to grid market and new energy vehicle charging prediction technology. In future work, we will place greater emphasis on designing high-performance and interpretable models to explore the fundamental reasons behind different charging trends in new energy vehicles.