In response to the increasing number of charging pile constructions and the tendency of equipment devia-tions,a method for relative error prediction of direct current charging piles based on deep learning algorithms is proposed in this paper.Firstly,the dataset collected from the direct current charging piles is preprocessed.Then,LightGBM,N-Linear and CNN models are constructed for relative error prediction,and MAE and MSE are adopted as evaluation metrics.The results indicate that the LightGBM model performs the best,with a decrease of 57.91%in MAE compared to the N-Linear model and a decrease of 30.16%compared to the CNN model.The MSE is reduced by 82.85%compared to the N-Linear model and approximately 47.32%compared to the CNN model.
deep learningrelative errorLightGBMtime series prediction
维普
万方数据
