Short Term Load Forecasting using Multi-layer Perception and Fuzzy Inference Systems for Islamic Countries

Short Term Load Forecasting (STLF) has received more and more attention during the last two decades because of economic reasons. In this study, for initial forecast we have developed a proper Multilayer Feedforward Neural Network (NN). This network has three layers and its parameters are tuned by Levenberg-Marquardt Bock Propagation (LMBP) augmented by an Early Stopping (ES) method to enhance speed of convergence of the learning algorithm. For abrupt weather changes and special holidays, a Fuzzy Inference System (FIS) has been also designed to improve the forecasted load appropriately. To show the effectiveness of the proposed method, some real experimental data taken from some Iranian electrical company have been considered in this study for the purpose of simulation. The results were very promising.