Research on load forecasting method of large Power Grid based on Deep confidence Network

Abstract

In view of the existing electric power load forecasting models exist in the measuring precision is not high and the disadvantage of low stability, this paper puts forward a deep belief network - based extreme learning machine (Deep Belief Network - Extreme Learning Machine, DBN - ELM) of power load forecasting model. On the basis of traditional DBN, this model introduces ELM as the regression layer, and combines DBN's advantages in feature extraction with ELM's strong generalization ability to improve the accuracy of power load prediction. The experiment shows that compared with the traditional DBN neural network, the model can better fit the variation trend of power load data time series, and has higher prediction accuracy.