Abstract:
This study presents the implementation of back propagation neural network method to improve forecasting of electricity load demand where the demand is highly dependent on various independent variables such as the weather, temperature, holidays, days of the week or even strikes. The implementation of this method requires mathematical software, data preparation and the calculation of degree of freedom, which is necessary for the neural networks architecture. We also consider the use of various combinations of activation functions in input layer to hidden layer and hidden layer to output layer and using analysis of variance and multiple comparison using Duncan`s tests to analyze the neural network`s performance. Two modifications to the backpropagation methods were developed to improve error with selected activation functions and a new improved error using mean square error. The data used are the daily electricity load demand for Malaysian from 2006 to 2007. The forecast accuracy based on the error statistics of forecast between the models for a month ahead is presented and behaviour of data is also observed.