Backpropagation neural networks for modeling gasoline consumption

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dc.contributor.author Nasr, George
dc.contributor.author Badr, E.A.
dc.contributor.author Joun, C.
dc.date.accessioned 2016-02-23T07:48:51Z
dc.date.available 2016-02-23T07:48:51Z
dc.date.copyright 2003
dc.date.issued 2016-02-23
dc.identifier.issn 0196-8904 en_US
dc.identifier.uri http://hdl.handle.net/10725/3158
dc.description.abstract This paper presents an artificial neural network (ANN) approach to gasoline consumption (GC) forecasting in Lebanon. In order to provide the forecasted gasoline consumption, the ANN interpolates among the GC and its determinants in a training data set. In this study, four ANN models are presented and implemented on real GC data. The first model is a univariate model based on past consumption values. The second model is a multivariate model based on GC time series and price (P). The third model is also a multivariate model based on GC and car registration (CR). Finally, the fourth model combines GC, P and CR. Forecasting performance measures, such as mean square errors and mean absolute deviations, are presented for all models. en_US
dc.language.iso en en_US
dc.title Backpropagation neural networks for modeling gasoline consumption en_US
dc.type Article en_US
dc.description.version Published 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 Energy Conversion and Management en_US
dc.journal.volume 44 en_US
dc.journal.issue 6 en_US
dc.article.pages 893-905 en_US
dc.keywords Gasoline consumption en_US
dc.keywords Forecasting en_US
dc.keywords Modeling en_US
dc.keywords Neural networks en_US
dc.identifier.doi http://dx.doi.org/10.1016/S0196-8904(02)00087-0 en_US
dc.identifier.ctation Nasr, G. E., Badr, E. A., & Joun, C. (2003). Backpropagation neural networks for modeling gasoline consumption. Energy Conversion and Management, 44(6), 893-905. en_US
dc.author.email genasr@lau.edu.lb
dc.identifier.url http://www.sciencedirect.com/science/article/pii/S0196890402000870

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