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dc.contributor.authorRomán Portabales, Antón
dc.contributor.authorLópez Nores, Martín 
dc.contributor.authorPazos Arias, José Juan 
dc.date.accessioned2021-10-21T12:33:02Z
dc.date.available2021-10-21T12:33:02Z
dc.date.issued2021-07-02
dc.identifier.citationSensors, 21(13): 4544 (2021)spa
dc.identifier.issn14248220
dc.identifier.urihttp://hdl.handle.net/11093/2602
dc.description.abstractThe forecast of electricity demand has been a recurrent research topic for decades, due to its economical and strategic relevance. Several Machine Learning (ML) techniques have evolved in parallel with the complexity of the electric grid. This paper reviews a wide selection of approaches that have used Artificial Neural Networks (ANN) to forecast electricity demand, aiming to help newcomers and experienced researchers to appraise the common practices and to detect areas where there is room for improvement in the face of the current widespread deployment of smart meters and sensors, which yields an unprecedented amount of data to work with. The review looks at the specific problems tackled by each one of the selected papers, the results attained by their algorithms, and the strategies followed to validate and compare the results. This way, it is possible to highlight some peculiarities and algorithm configurations that seem to consistently outperform others in specific settings.eng
dc.description.sponsorshipXunta de Galicia | Ref. ED431B 2020/34spa
dc.description.sponsorshipMinisterio de Educación y Ciencia | Ref. TIN2017-87604-Rspa
dc.language.isoengspa
dc.publisherSensorsspa
dc.rightsAttribution 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.titleSystematic review of electricity demand forecast using ANN-based machine learning algorithmseng
dc.typearticlespa
dc.rights.accessRightsopenAccessspa
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2017-87604-R/ESspa
dc.identifier.doi10.3390/s21134544
dc.identifier.editorhttps://www.mdpi.com/1424-8220/21/13/4544spa
dc.publisher.departamentoEnxeñaría telemáticaspa
dc.publisher.grupoinvestigacionGrupo de Servicios para la Sociedad de la Informaciónspa
dc.subject.unesco1203.02 Lenguajes Algorítmicosspa
dc.subject.unesco3325 Tecnología de las Telecomunicacionesspa
dc.subject.unesco3306 Ingeniería y Tecnología Eléctricasspa
dc.date.updated2021-10-07T07:14:41Z


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    Attribution 4.0 International
    Except where otherwise noted, this item's license is described as Attribution 4.0 International