A Vertical Federated Learning Method for Electric Vehicle Charging Station Load Prediction in Coupled Transportation and Power Distribution Systems

The continuous growth of electric vehicle (EV) ownership has increased the proportion of EV charging station load (EVCSL) in the distribution network (DN). The prediction of EVCSL is important for the safe and stable operation of the DN. However, simply predicting the EVCSL based on the characteristics of the DN, ignoring the impact of coupled transportation network (TN) characteristics, will reduce prediction performance. Few studies focus on combining DN and TN data for EVCSL prediction. On the premise of protecting the privacy of TN data, this paper proposes a vertical adaptive attention-based federated prediction method of EVCSL based on an edge aggregation graph attention network combined with a long- and short-term memory network (V2AFedEGAT combined with LSTM) to fully utilize the characteristics of DN and TN. This method introduces a spatio-temporal hybrid attention module to alleviate the characteristic distribution skew of DN and TN. Furthermore, to balance the privacy protection and training efficiency after multiple modules are integrated into the secure federated linear regression framework, the training strategy of the federated framework and the update strategy of the model are optimized. The simulation results show that the proposed federated method improves the prediction performance by about 4% and has a sub-second response speed.