Neural networks in forecasting electrical energy consumption

LAUR Repository

Show simple item record

dc.contributor.author Nasr, George
dc.contributor.author Badr, E.A.
dc.contributor.author Younes, M.R.
dc.date.accessioned 2016-02-23T08:16:19Z
dc.date.available 2016-02-23T08:16:19Z
dc.date.copyright 2002
dc.date.issued 2016-02-23
dc.identifier.issn 0363-907X en_US
dc.identifier.uri http://hdl.handle.net/10725/3160
dc.description.abstract This paper presents an artificial neural network (ANN) approach to electric energy consumption (EEC) forecasting in Lebanon. In order to provide the forecasted energy consumption, the ANN interpolates among the EEC and its determinants in a training data set. In this study, four ANN models are presented and implemented on real EEC data. The first model is a univariate model based on past consumption values. The second model is a multivariate model based on EEC time series and a weather-dependent variable, namely, degree days (DD). The third model is also a multivariate model based on EEC and a gross domestic product (GDP) proxy, namely, total imports (TI). Finally, the fourth model combines EEC, DD and TI. Forecasting performance measures such as mean square errors (MSE), mean absolute deviations (MAD), mean percentage square errors (MPSE) and mean absolute percentage errors (MAPE) are presented for all models. Copyright © 2002 John Wiley & Sons, Ltd. en_US
dc.language.iso en en_US
dc.title Neural networks in forecasting electrical energy consumption en_US
dc.type Article en_US
dc.description.version Published en_US
dc.title.subtitle Univariate and multivariate approaches en_US
dc.author.school SOE en_US
dc.author.idnumber 199390170 en_US
dc.author.woa N/A en_US
dc.author.department Electrical Engineering en_US
dc.description.embargo N/A en_US
dc.relation.journal International journal of energy research en_US
dc.journal.volume 26 en_US
dc.journal.issue 1 en_US
dc.article.pages 67-78 en_US
dc.identifier.doi http://dx.doi.org/10.1002/er.766 en_US
dc.identifier.ctation Nasr, G. E., Badr, E. A., & Younes, M. R. (2002). Neural networks in forecasting electrical energy consumption: univariate and multivariate approaches. International Journal of Energy Research, 26(1), 67-78. en_US
dc.author.email genasr@lau.edu.lb
dc.identifier.url http://onlinelibrary.wiley.com/doi/10.1002/er.766/full

Files in this item

Files Size Format View

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record

Search LAUR

Advanced Search


My Account