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dc.contributor.authorHernández Callejo, Luis 
dc.contributor.authorBaladrón García, Carlos 
dc.contributor.authorAguiar Pérez, Javier Manuel 
dc.contributor.authorCarro Martínez, Belén 
dc.contributor.authorSánchez Esguevillas, Antonio Javier
dc.contributor.authorLloret, Jaime
dc.date.accessioned2024-10-04T11:54:09Z
dc.date.available2024-10-04T11:54:09Z
dc.date.issued2014
dc.identifier.citationEnergy, October 2014, vol. 75, p. 252-264.es
dc.identifier.issn0360-5442es
dc.identifier.urihttps://uvadoc.uva.es/handle/10324/70424
dc.descriptionProducción Científicaes
dc.description.abstractThe adaptation of energy production to demand has been traditionally very important for utilities in order to optimize resource consumption. This is especially true also in microgrids where many intelligent elements have to adapt their behaviour depending on the future generation and consumption conditions. However, traditional forecasting has been performed only for extremely large areas, such as nations and regions. This work aims at presenting a solution for short-term load forecasting (STLF) in microgrids, based on a three-stage architecture which starts with pattern recognition by a self-organizing map (SOM), a clustering of the previous partition via k-means algorithm, and finally demand forecasting for each cluster with a multilayer perceptron. Model validation was performed with data from a microgrid-sized environment provided by the Spanish company Iberdrola.es
dc.format.mimetypeapplication/pdfes
dc.language.isoenges
dc.publisherElsevieres
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subject.classificationArtificial neural networkes
dc.subject.classificationShort-term load forecastinges
dc.subject.classificationMicrogrides
dc.subject.classificationPattern recognitiones
dc.subject.classificationSelf-organizing mapes
dc.subject.classificationk-Means algorithmes
dc.titleArtificial neural networks for short-term load forecasting in microgrids environmentes
dc.typeinfo:eu-repo/semantics/articlees
dc.rights.holder© Elsevieres
dc.identifier.doi10.1016/j.energy.2014.07.065es
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S0360544214008871?via%3Dihub#ack0010es
dc.identifier.publicationfirstpage252es
dc.identifier.publicationlastpage264es
dc.identifier.publicationtitleEnergyes
dc.identifier.publicationvolume75es
dc.peerreviewedSIes
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.type.hasVersioninfo:eu-repo/semantics/acceptedVersiones
dc.subject.unesco3306 Ingeniería y Tecnología Eléctricases


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