A Comparative Analysis of Neural Networks and Enhancement of ELM for Short Term Load Forecasting

Abstract

Smart grid is the evolved form of traditional power grid with the integration of sensing, communication, computing, monitoring and controlling technologies. These technologies make the power grid reliable, efficient and economical. Smart grid enable users to make bidding on the basis of demand side management models. Demand side management can be made responsive and efficient by effective and accurate load forecasting. Accurate load forecasting is an important but challenging task because of irregular and non-linear consumption of individual users and industrial consumers. Different approaches have been proposed for load forecasting, but artificial intelligent models, specifically ANNs perform well for short, medium and long term load forecasting. The main focus of this paper is to present a comparative analysis of NNs for short term load forecasting. NYISO dataset is used for experiments. XGboost and decision tree are used for features importance calculation and RFE is used for features extraction on the basis of score assigned. Three basic techniques (CNN, MLP and ELM) are used for forecasting. Furthermore, ELM is enhanced and E_ELM is proposed for STLF. Moreover, results are evaluated on four statistical measures (MAPE, MAE, MSE and RMS).