ParDeeB: A Graph Framework for Load Forecasting Based on Parallel DeepNet Branches

Abstract

Recently, energy demand forecasting has emerged as a significant area of research because of its prominent impact on greenhouse gases (GHGs) emission and global warming. The problems of load forecasting are characterized by complex and nonlinear nature and also long-term historical dependency.Up to now, several approaches from statistical to computational intelligent have been applied in this research filed. The literature agrees with the fact that deep learning approach is more capable in dealing with these characteristics among existing approaches. However, the recent state-of-the-art deep network models are not robust against different historical dependency. In this