A Charging Station Fault Prediction Method based on Improved Stacking Multi-Model Fusion

To improve the accuracy of electric vehicle charging station fault prediction, a method based on improved Stacking with multiple model fusion is proposed. Firstly, the operational parameters of the charging station are preprocessed, and the processed data is used as input for the models. Subsequently, multiple algorithms, optimized using particle swarm optimization, are employed to build fault prediction models. The algorithm selection is based on the importance from random forest and Pearson correlation coefficient, considering the importance and predictive correlation of each model. Unnecessary models are eliminated, and the final ensemble of base learners is determined. The outputs generated by the base learner models through 5-fold cross-validation are used as inputs for training the meta-learner in the second layer, establishing the Stacking prediction model. Finally, a real dataset of charging station faults is used for simulation analysis. The simulation results demonstrate that the proposed method performs exceptionally well, achieving a fault prediction accuracy of 96.38%.