Research on short-term line loss rate prediction method of distribution network based on RF-CNN-LSTM

Under the background of the new distribution network, the power fluctuation on the line is increasing, which leads to more uncertainties in the predicted line loss rate, thus affecting the economic benefits of the power grid. In order to reduce the prediction error of short-term line loss rate and improve its prediction accuracy, this paper studies a short-term line loss rate prediction method of distribution network based on RF-CNN-LSTM. Firstly, this paper comprehensively considers the influence of various uncertain factors on the accuracy of prediction results. Aiming at the characteristics of high-dimensional time series of line loss rate data, a random forest (RF) algorithm is proposed to analyze the importance of multiple characteristic variables affecting line loss rate. Then, this paper constructs a combined model of convolutional neural network and long short-term memory network (CNN-LSTM) to predict line loss rate. Finally, in order to verify the accuracy of the prediction results, this paper sets up a support vector machine algorithm for synchronous prediction as a comparative experiment. The experimental results show that the prediction results of the proposed prediction method are more accurate.