Short Term Load Forecasting Using BPN and RBF Network

Abstract

Simple neural network based short term load forecaster is designed which predicts load values to be obtained beforehand. The neural network based short term load forecaster has two modules (1). Back Propagation Network (BPN) and (2) Radial Basis Function Network (RBF). The inputs used were the actual hourly load demand for the full day (24 hours) and the outputs obtained were the predicted hourly load demand for the next day.  The number of inputs is 25 while the number of hidden layer neurons is varied for different performance of the network and the output layer has 24 neurons. The results obtained from two different approaches are compared and accuracy of neural network is reported better.  Also, the network has been trained over one week and an absolute mean error of 2.64% was achieved when the trained network was tested on one week’s data. Short-term hourly load forecasting is predicted using Matlab R2010a toolbox.