Univariate modeling and forecasting of energy consumption

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dc.contributor.author Saab, Samer
dc.contributor.author Badr, Elie
dc.contributor.author Nasr, George
dc.date.accessioned 2016-02-23T08:36:29Z
dc.date.available 2016-02-23T08:36:29Z
dc.date.copyright 2001
dc.date.issued 2016-02-23
dc.identifier.issn 0360-5442 en_US
dc.identifier.uri http://hdl.handle.net/10725/3161
dc.description.abstract In Lebanon, electric power is becoming the main energy form relied upon in all economic sectors of the country. Also, the time series of electrical energy consumption in Lebanon is unique due to intermittent power outages and increasing demand. Given these facts, it is critical to model and forecast electrical energy consumption. The aim of this study is to investigate different univariate-modeling methodologies and try, at least, a one-step ahead forecast for monthly electric energy consumption in Lebanon. Three univariate models are used, namely, the autoregressive, the autoregressive integrated moving average (ARIMA) and a novel configuration combining an AR(1) with a highpass filter. The forecasting performance of each model is assessed using different measures. The AR(1)/highpass filter model yields the best forecast for this peculiar energy data. en_US
dc.language.iso en en_US
dc.title Univariate modeling and forecasting of energy consumption en_US
dc.type Article en_US
dc.description.version Published en_US
dc.title.subtitle the case of electricity in Lebanon en_US
dc.author.idnumber 199690250 en_US
dc.author.idnumber 199390170
dc.author.department Electrical Engineering en_US
dc.description.embargo N/A en_US
dc.relation.journal Energy en_US
dc.journal.volume 26 en_US
dc.journal.issue 1 en_US
dc.article.pages 1-14 en_US
dc.identifier.doi http://dx.doi.org/10.1016/S0360-5442(00)00049-9 en_US
dc.identifier.ctation Saab, S., Badr, E., & Nasr, G. (2001). Univariate modeling and forecasting of energy consumption: the case of electricity in Lebanon. Energy, 26(1), 1-14. en_US
dc.author.email ssaab@lau.edu.lb
dc.author.email genasr@lau.edu.lb
dc.identifier.url http://www.sciencedirect.com/science/article/pii/S0360544200000499
dc.orcid.id https://orcid.org/0000-0003-0124-8457 en_US

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