Short-Term Load Forecasting Using Artificial Neural Network

Abstract

Artificial neural network (ANN) has been used for many years in sectors and disciplines like medical science, defence industry, robotics, electronics, economy, forecasts, etc. The learning property of ANN in solving nonlinear and complex problems called for its application to forecasting problems. This report present the development of an ANN based short-term load forecasting model for the 132/33KV sub- Station, Kano, Nigeria. The recorded daily load profile with a lead time of 1-24 hours for the year 2005 was obtained from the utility company. The Levenberg-Marquardt optimization technique which has one of the best learning rates was used as a back propagation algorithm for the Multilayer Feed Forward ANN model using MATLAB ® R2008b ANN Toolbox. Experiences gained during the development of the model regarding the selection of the input variables, the ANN structure, and the training parameters are described. The forecasted next day 24 hourly peak loads are obtained based on the stationary output of the ANN with a performance Mean Squared Error (MSE) of ૞.ૡ૝ࢋ ૟ and compares favorably with the actual Power utility data. The results have shown that the proposed technique is robust in forecasting future load demands for the daily operational planning of power system distribution sub-stations in Nigeria.