Application of Computation Intelligence Techniques for Energy Load and Price Forecast in some States of USA

Abstract

The purpose of this paper is to forecast the load and the price of electricity, 49 hours ahead. To accomplish these goals, computational intelligence techniques were used, specifically artificial neural networks and genetic algorithms. The neural networks employed are RBFs (radial basis functions), fully connected and with just one hidden layer. The genetic algorithm used was MOGA (multiple objective genetic algorithm), which, as the name indicates, minimizes not a single objective but several. The neural networks are trained for one step ahead, and its output is feedback until 49 hours are calculated. MOGA is used for the input selection and for topology determination. The data used was kindly given by the University of Auburn, USA, and refers to real data from some North-American states.