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dc.contributor.authorMariño, Maria D.spa
dc.contributor.authorArango, Adrianaspa
dc.contributor.authorLotero, Lauraspa
dc.contributor.authorJimenez, Maritzaspa
dc.date.accessioned2020-12-31 14:30:36
dc.date.accessioned2022-06-17T20:20:59Z
dc.date.available2020-12-31 14:30:36
dc.date.available2022-06-17T20:20:59Z
dc.date.issued2020-12-31
dc.identifier.issn1794-1237
dc.identifier.urihttps://repository.eia.edu.co/handle/11190/5130
dc.description.abstractPronosticar la demanda eléctrica es de suma importancia para la planeación estratégica de una nación. La literatura ofrece múltiples acercamientos para el desarrollo de modelos de pronóstico enfocados principalmente en la demanda nacional agregada, dejando de lado los análisis sectoriales, en particular a los sectores no residenciales. En este artículo, utilizando la metodología de análisis de Series de Tiempo, se ajustan, validan y comparan tres diferentes modelos para pronosticar la demanda eléctrica del sector minas y canteras, uno de los más representativos en el consumo eléctrico colombiano. Los modelos ajustados incluyen un modelo de componentes aditivo, un SARIMA y un Holt Wiatednters. Los resultados indican que el modelo que presenta un menor error de pronóstico es el modelo Holt Winters.spa
dc.description.abstractDemand forecasting is of utmost importance for strategic decision making of a nation. Literature offers multiple approaches to the development of forecast models focused in aggregate demand, also, little attention has been paid to non-residential sector demand forecasts. In this paper, using Time Series Analysis approach, three different models are fitted, tested and compared to forecast electricity demand in mining and quarrying sector, one of the most representative non-residential sector for colombian electricity demand. Fitted models include an additive model, a SARIMA and a Holt Winters model. Results indicate that better accuracy is provided the by Holt Winters model.eng
dc.format.mimetypeapplication/pdfspa
dc.language.isospaspa
dc.publisherFondo Editorial EIA - Universidad EIAspa
dc.rightsRevista EIA - 2020spa
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0spa
dc.sourcehttps://revistas.eia.edu.co/index.php/reveia/article/view/1458spa
dc.subjectTime Serieseng
dc.subjectForecasting Modelseng
dc.subjectElectricity Demandeng
dc.subjectMining and Quarryingeng
dc.subjectHolt Winterseng
dc.subjectSARIMAeng
dc.subjectAdditive Modeleng
dc.subjectColombiaeng
dc.subjectPlanningeng
dc.subjectStrategyeng
dc.subjectSeries de Tiempospa
dc.subjectModelos de Pronósticospa
dc.subjectDemanda Eléctricaspa
dc.subjectMinas y Canterasspa
dc.subjectHolt Wintersspa
dc.subjectSARIMAspa
dc.subjectModelo de Componentesspa
dc.subjectColombiaspa
dc.subjectPlaneaciónspa
dc.subjectEstrategiaspa
dc.titleModelos de series temporales para pronóstico de la demanda eléctrica del sector de explotación de minas y canteras en Colombiaspa
dc.typeArtículo de revistaspa
dc.typeJournal articleeng
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dc.identifier.doi10.24050/reia.v18i35.1458
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.creativecommonsEsta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.spa
dc.type.coarhttp://purl.org/coar/resource_type/c_6501spa
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dc.type.driverinfo:eu-repo/semantics/articlespa
dc.type.versioninfo:eu-repo/semantics/publishedVersionspa
dc.identifier.eissn2463-0950
dc.identifier.urlhttps://doi.org/10.24050/reia.v18i35.1458
dc.relation.bitstreamhttps://revistas.eia.edu.co/index.php/reveia/article/download/1458/1378
dc.relation.citationeditionNúm. 35 , Año 2021spa
dc.relation.citationendpage23
dc.relation.citationissue35spa
dc.relation.citationstartpage35007 pp. 1
dc.relation.citationvolume18spa
dc.relation.ispartofjournalRevista EIAspa
dc.title.translatedTime series forecasting for Colombian mining and quarrying electricity demandeng
dc.type.contentTextspa
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