Application of multilayer perceptron with backpropagation algorithm and regression analysis for long-term forecast of electricity demand: A comparison

Bong, David B L and Tan, J.Y.B. and Lai, K.C. (2009) Application of multilayer perceptron with backpropagation algorithm and regression analysis for long-term forecast of electricity demand: A comparison. International Conference on Electronic Design, 2008. ICED 2008. ISSN ISBN: 978-1-4244-2315-6

[img]
Preview
PDF
Application of Multilayer Perceptron with Backpropagation Algorithm (abstract).pdf

Download (351kB) | Preview
Official URL: http://ieeexplore.ieee.org/document/4786748/

Abstract

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.

Item Type: Article
Uncontrolled Keywords: Multilayer perceptrons, Backpropagation algorithms, Regression analysis, Economic forecasting, Artificial neural networks, Power generation economics, Social factors, Economic indicators, Power supplies, Predictive models, unimas, university, universiti, Borneo, Malaysia, Sarawak, Kuching, Samarahan, ipta, education, research, Universiti Malaysia Sarawak
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Academic Faculties, Institutes and Centres > Faculty of Engineering
Depositing User: Karen Kornalius
Date Deposited: 14 Jun 2017 07:04
Last Modified: 14 Jun 2017 07:04
URI: http://ir.unimas.my/id/eprint/16644

Actions (For repository members only: login required)

View Item View Item