Short-term load forecasting using a novel deep learning framework

Abstract

Short-term load forecasting is the basis of power system operation and analysis. In recent years, the use of a deep belief network (DBN) for short-term load forecasting has become increasingly popular. In this study, a novel deep-learning framework based on a restricted Boltzmann machine (RBM) and an Elman neural network is presented. This novel framework is used for short-term load forecasting based on the historical power load data of a town in the UK. The obtained results are compared with an individual use of a DBN and Elman neural network. The experimental results demonstrate that our proposed model can significantly ameliorate the prediction accuracy.