An Accurate and Fast Converging Short-Term Load Forecasting Model for Industrial Applications in a Smart Grid

Abstract

Short-term load forecasting (STLF) models are very important for electric industry in the trade of energy. These models have many applications in the day-to-day operations of electric utilities such as energy generation planning, load switching, energy purchasing, infrastructure maintenance, and contract evaluation. A large variety of STLF models have been developed that trade off between forecast accuracy and convergence rate. This paper presents an accurate and fast converging STLF model for industrial applications in a smart grid. In order to improve the forecast accuracy, modifications are devised in two popular techniques: mutual information based feature selection; and enhanced differential evolution algorithm based error minimization. On the other hand, the convergence rate of the overall forecast strategy is enhanced by devising modifications in the heuristic algorithm and in the training process of the artificial neural network. Simulation results show that accuracy of the newly proposed forecast model is 99.5% with moderate execution time, i.e., we have decreased the average execution of the existing bilevel forecast strategy by 52.38%.