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
维普
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