A daily line loss prediction method for medium and low voltage distribution networks based on improved LSTM
In order to solve the problems of low accuracy of daily line loss prediction,lack of original power data and outliers in the current low-voltage distribution network,a two-stage line loss rate prediction method including data preprocessing and improved LSTM prediction network and a method based on GAN to expand the sample and increase the sample diversity were proposed.The improved LSTM prediction network was an R-CNN deep learning network architecture with multi-layer LSTM,which can extract features and temporal dimension information from power data.Through experiments,compared with Bi LSTM,LSTM autoencoder,CNN-GRU,BL-Seq2seq,the pro-posed prediction network had the best comprehensive performance in terms of RMSE,MAE,RA2,and training time indicators.The experimental results showed that the proposed prediction network can achieve better prediction ac-curacy in predicting the daily line loss rate of low-voltage distribution networks,and the model training time is the shortest.
low voltage distribution networkline lossdeep learningconvolutional neural networksrecurrent neu-ral network
万方数据
维普
