Bidirectional Coupling Prediction Algorithm for Vehicle-Grid Load Power Based on Knowledge Graph and BiLSTM

This paper addresses the current issue of charging load forecasting that does not consider the bidirectional coupling of electric vehicle (EV) and charging pile load power, and proposes a bidirectional coupling prediction algorithm for vehicle-grid load power based on knowledge graph and BiLSTM. Firstly, in response to the vast amount of data from EVs and charging piles, as well as the difficulty in integrating data between EVs, power grids, and road networks, this paper constructs a vehicle-roadnetwork coupled three-domain knowledge graph fusion architecture. Secondly, this paper proposes a bidirectional coupling prediction algorithm for vehicle-grid load power based on knowledge graph and BiLSTM to enhance the accuracy of charging pile load forecasting. Finally, this paper conducts a case study analysis for the proposed model, verifying through a comparison with single load forecasting that the model proposed in this paper can quickly achieve accurate prediction of charging load power, thereby further improving the new energy consumption rate.