Short-term daily peak load forecasting using fast learning neural network

Abstract

Load forecasting has been an inevitable issue in electric power supply in past. It is always desired to predict the load requirements in order to generate and supply electric power efficiently. In this research, a neuro-evolutionary technique known as Cartesian Genetic Algorithm evolved Artificial Neural Network (CGPANN) has been deployed to develop a peak load forecasting model for the prediction of peak loads 24 hours ahead. The proposed model presents the training of all the parameters of Artificial Neural Network (ANN) including: weights, topology and functionality of individual nodes. The network is trained both on annual as well as quarterly bases, thus obtaining a unique model for each season.