The Adaptive Neuro-Fuzzy Inference System Model for Short-Term Load, Price, and Topology Forecasting of Distribution System

Abstract

This book chapter presents a short-term load, locational marginal price, and optimal topology-forecasting procedure for a distribution system that utilizes fossil-fueled distributed generation units. An intelligent hybrid model is proposed for forecasting of price and load based on mutual information and Kalman-Kohonen feature selection. The proposed method uses an adaptive neuro-fuzzy inference system. Further, the optimal topology of the system is forecasted based on the historical data and co-active neuro-fuzzy inference system. The feature selection determines the most proper inputs among a huge historical data. The proposed feature selection is based on Kalman-Kohonen model for load forecasting and adaptive neuro-fuzzy inference system model for price forecasting. The obtained results for a distribution system confirmed the model’s effective performance.