Research on Power Load Forecasting Using Deep Neural Network and Wavelet Transform

Abstract

In today's rapid economic development, industrial and civil electricity consumption is growing year by year, and how to guarantee stability of power system operation has become the focus of attention of the power sector in each country. Power load forecasting has been closely associated with the modernization of power system management and is a vital guarantee for the safe and stable operation and economic efficiency of the power system. In this article, the authors propose a recurrent neural network (RNN) decision fusion forecasting framework based on the wavelet transform to address the power load forecasting problem. The framework firstly performs the wavelet transform on the power load data and uses Daubechies wavelets to extract the high-frequency and low-frequency parts of the data; then the data with different frequencies are combined with the original data and fed into the RNN model separately, and the decision fusion is performed in the output layer; finally, the prediction results are obtained by superposition of two RNN networks. The results showed that the error of the predicted data in the last nine years decreased by 50%, compared with the traditional method of feeding the data into the RNN model for training, which provides a new idea for future power load forecasting.