Temporary variables for predicting electricity consumption through data mining

dc.creatorsilva d, jesus g
dc.creatorSenior Naveda, Alexa
dc.creatorHernández Palma, Hugo
dc.creatorNiebles Núñez, William
dc.creatorNiebles Nuñez, Leonardo David
dc.date2020-01-30T13:38:21Z
dc.date2020-01-30T13:38:21Z
dc.date2020
dc.date.accessioned2023-10-03T19:53:31Z
dc.date.available2023-10-03T19:53:31Z
dc.identifier1742-6596
dc.identifier1742-6588
dc.identifierhttp://hdl.handle.net/11323/5947
dc.identifierCorporación Universidad de la Costa
dc.identifierREDICUC - Repositorio CUC
dc.identifierhttps://repositorio.cuc.edu.co/
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/9173033
dc.descriptionIn 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.
dc.formatapplication/pdf
dc.formatapplication/pdf
dc.languageeng
dc.publisherJournal of Physics: Conference Series
dc.relation10.1088/1742-6596/1432/1/012033/pdf
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dc.rightsCC0 1.0 Universal
dc.rightshttp://creativecommons.org/publicdomain/zero/1.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.subjectElectricity
dc.subjectTemporary Variables
dc.subjectData mining
dc.titleTemporary variables for predicting electricity consumption through data mining
dc.typeArtículo de revista
dc.typehttp://purl.org/coar/resource_type/c_6501
dc.typeText
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typehttp://purl.org/redcol/resource_type/ART
dc.typeinfo:eu-repo/semantics/acceptedVersion
dc.typehttp://purl.org/coar/version/c_ab4af688f83e57aa


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