Application of multilayer perceptron with backpropagation algorithm and regression analysis for long-term forecast of electricity demand: A comparison - Bong, David B L; Tan, J.Y.B.; Lai, K.C. (IEEE, 2009)
Having an accurate forecast of future electricity usage is vital for utility companies to be able to provide adequate power supply to meet the demand. Two methods have been implemented to perform forecasting of electricity demand, namely, regression analysis (RA) and artificial neural networks (ANNs). We aim to compare these two methods in this paper using the mean absolute percentage error (MAPE) to measure the forecasting performance. The results show that ANNs are more effective than RA in long-term forecast. In addition to that, from our investigation into the effects of the inclusion of economic and social factors, such as population and gross domestic product (GDP), into the forecast, we conclude that the inclusion of economic and social factors do not improve the accuracy of the forecast of the chosen ANN model for electricity demand.