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dc.contributor.authorBessec, Marie
dc.contributor.authorFouquau, Julien
dc.date.accessioned2017-11-22T15:42:40Z
dc.date.available2017-11-22T15:42:40Z
dc.date.issued2018
dc.identifier.issn0377-2217
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/17023
dc.language.isoenen
dc.subjectForecastingen
dc.subjectelectricity demanden
dc.subjectseasonalityen
dc.subjectwavelet transformen
dc.subjectcombinationsen
dc.subject.ddc338en
dc.subject.classificationjelL.L9.L94en
dc.subject.classificationjelE.E1.E17en
dc.titleShort-run electricity load forecasting with combinations of stationary wavelet transformsen
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenShort-term forecasting of electricity load is an essential issue for the management of power systems and for energy trading. Specific modeling approaches are needed given the strong seasonality and volatility in load data. In this paper, we investigate the benefit of combining stationary wavelet transforms to produce one day-ahead forecasts of half-hourly electric load in France. First, we assess the advantage of decomposing the aggregate load into several subseries with a wavelet transform. Each component is predicted separately and aggregated to get the final forecast. One innovation of this paper is to propose several approaches to deal with the boundary problem which is particularly detrimental in electricity load forecasting. Second, we examine the benefit of combining forecasts over individual models. An extensive out-of-sample evaluation shows that a careful treatment of the border effect is required in the multiresolution analysis. Combinations including the wavelet predictions provide the most accurate forecasts. This result is valid with several assumptions about the forecast error in temperature and for different types of hours (peak, normal, off-peak), different days of the week and various forecasting periods.en
dc.relation.isversionofjnlnameEuropean Journal of Operational Research
dc.relation.isversionofjnlvol264en
dc.relation.isversionofjnlissue1en
dc.relation.isversionofjnldate2018
dc.relation.isversionofjnlpages149-64en
dc.relation.isversionofdoi10.1016/j.ejor.2017.05.037en
dc.relation.isversionofjnlpublisherElsevieren
dc.subject.ddclabelEconomie industrielleen
dc.relation.forthcomingnonen
dc.relation.forthcomingprintnonen
dc.description.ssrncandidatenonen
dc.description.halcandidateouien
dc.description.readershiprechercheen
dc.description.audienceInternationalen
dc.relation.Isversionofjnlpeerreviewedouien
dc.relation.Isversionofjnlpeerreviewedouien
dc.date.updated2017-11-22T15:35:08Z
hal.person.labIds215495
hal.person.labIds
hal.identifierhal-01644930*


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