Short-term power load forecasting based on RF-CNN-SVM

Abstract

In order to reduce the error of short-term power load forecasting and improve its forecasting accuracy, A prediction method based on the combination of random forest (RF), convolution neural network (CNN) and support vector machine (SVM) is proposed. First, the data is preprocessed, and the RF algorithm is introduced to optimize the input variables, Then the feature is extracted through CNN, Finally, the extracted results are input into the SVM model, and the forecasting results are output to realize the load forecasting. In this paper, the power load data of Singapore is used for experimental analysis, compared with CNN-SVM model without RF algorithm, SVM model and hybrid model of convolutional neural network and long short-term memory network (CNN-LSTM), The results show that the prediction model method proposed in this paper has better prediction effect.