Short-term forecasting of Jordanian electricity demand using particle swarm optimization

Short-term electricity demand forecasting (i.e., the prediction of hourly loads (demand) for the next hour to several days out) is one of the most important tools by which an electric utility/company plans and dispatches the loading of generating units in order to meet system demand. The accuracy of the dispatching system, which is derived from the accuracy of the forecasting algorithm used, will determine the economics of the operation of the power system. The inaccuracy or large error in the forecast simply means that load matching is not optimized and consequently the generation and transmission systems are not being operated in an efficient manner. This paper discusses and presents the results of utilizing neural network for forecasting the Jordanian electricity demand that is trained by particle swarm optimization technique, which is a new adaptive algorithm based on a social-psychological metaphor. The results of using this technique are compared with the results of using back-propagation algorithm and autoregressive moving average method. © 2007 Elsevier B.V. All rights reserved.