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dc.contributor.authorsilva d, jesus gspa
dc.contributor.authorSenior Naveda, Alexaspa
dc.contributor.authorHernández Palma, Hugospa
dc.contributor.authorNiebles Núñez, Williamspa
dc.contributor.authorNiebles Nuñez, Leonardo Davidspa
dc.date.accessioned2020-01-30T13:38:21Z
dc.date.available2020-01-30T13:38:21Z
dc.date.issued2020
dc.identifier.issn1742-6596spa
dc.identifier.issn1742-6588spa
dc.identifier.urihttp://hdl.handle.net/11323/5947spa
dc.description.abstractIn the new global and local scenario, the advent of intelligent distribution networks or Smart Grids allows real-time collection of data on the operating status of the electricity grid. Based on this availability of data, it is feasible and convenient to predict consumption in the short term, from a few hours to a week. The hypothesis of the study is that the method used to present time variables to a prediction system of electricity consumption affects the results.spa
dc.language.isoeng
dc.publisherJournal of Physics: Conference Seriesspa
dc.relation.ispartof10.1088/1742-6596/1432/1/012033/pdfspa
dc.rightsCC0 1.0 Universalspa
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/spa
dc.subjectElectricityspa
dc.subjectTemporary Variablesspa
dc.subjectData miningspa
dc.titleTemporary variables for predicting electricity consumption through data miningspa
dc.typeArtículo de revistaspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.identifier.instnameCorporación Universidad de la Costaspa
dc.identifier.reponameREDICUC - Repositorio CUCspa
dc.identifier.repourlhttps://repositorio.cuc.edu.co/spa
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dc.type.contentTextspa
dc.type.driverinfo:eu-repo/semantics/articlespa
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dc.type.versioninfo:eu-repo/semantics/acceptedVersionspa
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dc.rights.coarhttp://purl.org/coar/access_right/c_abf2spa


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