Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/76069
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dc.contributor.authorEspino, I.en_US
dc.contributor.authorHernández, M.en_US
dc.date.accessioned2020-11-26T12:58:36Z-
dc.date.available2020-11-26T12:58:36Z-
dc.date.issued2011en_US
dc.identifier.issn2172-038Xen_US
dc.identifier.otherScopus-
dc.identifier.urihttp://hdl.handle.net/10553/76069-
dc.description.abstractThe aim of this paper is to describe and evaluate a proposal for nowcasting wind speed for wind farm locations from historical time series, based on the method of regression by support vectors. To show the improvement over other methods, we used the ANEMOS Project standard evaluation protocol for forecasting against three reference models to compare, referred to a statistical approach: persistence, autoregressive and autoregressive moving average models. The obtained results show a good performance of the proposed method and how beat the classical reference models.en_US
dc.languageengen_US
dc.relation.ispartofRenewable energy and power quality journalen_US
dc.sourceRenewable Energy and Power Quality Journal [EISSN 2172-038X], v. 1 (9), p. 700-705, (Mayo 2011)en_US
dc.subject120304 Inteligencia artificialen_US
dc.subject.otherNowcastingen_US
dc.subject.otherSupport Vector Regressionen_US
dc.subject.otherWind Speed Forecastingen_US
dc.titleNowcasting of wind speed using support vector regression. Experiments with time series from Gran canariaen_US
dc.typeinfo:eu-repo/semantics/Articleen_US
dc.typeArticleen_US
dc.identifier.doi10.24084/repqj09.428en_US
dc.identifier.scopus84937702512-
dc.contributor.authorscopusid57211283221-
dc.contributor.authorscopusid57212239402-
dc.identifier.eissn2172-038X-
dc.description.lastpage705en_US
dc.identifier.issue9-
dc.description.firstpage700en_US
dc.relation.volume1en_US
dc.investigacionIngeniería y Arquitecturaen_US
dc.type2Artículoen_US
dc.utils.revisionen_US
dc.date.coverdateMayo 2011en_US
dc.identifier.ulpgcen_US
item.fulltextCon texto completo-
item.grantfulltextopen-
crisitem.author.deptGIR SIANI: Inteligencia Artificial, Redes Neuronales, Aprendizaje Automático e Ingeniería de Datos-
crisitem.author.deptIU Sistemas Inteligentes y Aplicaciones Numéricas-
crisitem.author.deptDepartamento de Informática y Sistemas-
crisitem.author.orcid0000-0001-9717-8048-
crisitem.author.parentorgIU Sistemas Inteligentes y Aplicaciones Numéricas-
crisitem.author.fullNameHernández Tejera, Francisco Mario-
Colección:Artículos
miniatura
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