Short term Load Forecasting using Resource Allocation based Artificial Neural Network

Abstract

Load forecasts are extremely important for energy suppliers, financial institutions, and other participants in electric energy generation, transmission, distribution, and markets. Load forecasting helps an electric utility to make important decisions including decisions on purchasing and generating electric power, load switching, and infrastructure development. The use of artificial neural networks (ANN or simply NN) has been a widely studied electric load forecasting technique. These networks are essentially non-linear circuits that have the demonstrated capability to do non-linear curve fitting. The use of artificial neural network has received increased attention in recent years, because of its usefulness in reducing the needs for complex mathematical models in problem solving. In this paper we use a new approach for load forecasting using Radial-Basis function networks (RBF). These networks being the members of a class of neural network models address the problem of curve fitting that is approximation in high dimensional space that provides a best fit to the training data, measured by pre-selected statistical criteria. Because of this non-linear nature of these models, the behavior of the load prediction system can be captured in a compact, robust, and more natural representation. In the present work, resource allocation network (RAN), a type of RBF network with one hidden layer has been used as load forecasting model.