Recent Development in Electricity Price Forecasting Based on Computational Intelligence Techniques in Deregulated Power Market

The development of artificial intelligence (AI) based techniques for electricity price forecasting (EPF) provides essential information to market participants and managers because its greater handling capability complex input output relationships. Therefore, this research investigates analyzes the performance different optimization methods in training phase neural network (ANN) adaptive neuro-fuzzy inference system (ANFIS) accuracy enhancement EPF. In work, a multi-objective optimization-based feature selection technique with eliminating non-linear interacting features is implemented create an efficient day-ahead forecasting. beginning, binary backtracking search algorithm (MOBBSA)-based used examine various combinations variables choose suitable subsets, which minimizes, simultaneously, both number estimation error. later phase, selected are transferred into machine learning-based map order forecast price. Furthermore, increase accuracy, (BSA) applied as evolutionary learning procedure ANFIS approach. Queensland power year 2018, well-known most competitive world, investigated compared show superiority proposed over other methods.