DAE-LSTM neural network-based prediction model for segmental line loss rate of low-voltage power grid

Abstract

The traditional low-voltage grid segmental line loss rate prediction is more often chosen from the tidal method, but the operational limitations of the method lead to the low prediction accuracy of the method. In this regard, a low-voltage grid segmental loss rate prediction model based on the combination of DAE and LSTM neural network is proposed. Firstly, the DAE is used to encode the input content and extract the main features of the input content, and secondly, the encoded input content is put into the LSTM model for training to obtain the low-voltage segmental line loss rate prediction model. In the experiments, the prediction accuracy of the model is verified. The experimental analysis shows that the model has high prediction accuracy when the proposed model is used for LVR segmented line loss rate prediction.