Comparison between Adaptive and Conventional RBFNN Based Approach for Short-Term Load Forecasting

Abstract

In this paper, a comparison between novel adaptive Radial Basis Function Neural Networks (RBFNN) algorithm and conventional RBFNN is conducted. Both algorithms are used to forecast electrical load demand in Jordan. The Same forecasting features are used in both algorithms. Most of the forecasting models need to be adjusted after a period of time, because the change in the system parameters. The data used in this paper is real data measured by National Electrical Power co. (Jordan). The data is divided into two sets. Set for a training and the other for testing. The results illustrated that the adaptive RBFNN model outperformed conventional RBFNN. The proposed adaptive RBFNN model can enhance the reliability of the conventional RBFNN after embedding the network in the system. This is achieved by introducing an adaptive algorithm that allows the change of the weights of the RBFNN after the training process is completed, which will eliminate the need to retrain the RBFNN model again.