Identificador persistente para citar o vincular este elemento:
http://hdl.handle.net/10553/76069
Campo DC | Valor | idioma |
---|---|---|
dc.contributor.author | Espino, I. | en_US |
dc.contributor.author | Hernández, M. | en_US |
dc.date.accessioned | 2020-11-26T12:58:36Z | - |
dc.date.available | 2020-11-26T12:58:36Z | - |
dc.date.issued | 2011 | en_US |
dc.identifier.issn | 2172-038X | en_US |
dc.identifier.other | Scopus | - |
dc.identifier.uri | http://hdl.handle.net/10553/76069 | - |
dc.description.abstract | The 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.language | eng | en_US |
dc.relation.ispartof | Renewable energy and power quality journal | en_US |
dc.source | Renewable Energy and Power Quality Journal [EISSN 2172-038X], v. 1 (9), p. 700-705, (Mayo 2011) | en_US |
dc.subject | 120304 Inteligencia artificial | en_US |
dc.subject.other | Nowcasting | en_US |
dc.subject.other | Support Vector Regression | en_US |
dc.subject.other | Wind Speed Forecasting | en_US |
dc.title | Nowcasting of wind speed using support vector regression. Experiments with time series from Gran canaria | en_US |
dc.type | info:eu-repo/semantics/Article | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.24084/repqj09.428 | en_US |
dc.identifier.scopus | 84937702512 | - |
dc.contributor.authorscopusid | 57211283221 | - |
dc.contributor.authorscopusid | 57212239402 | - |
dc.identifier.eissn | 2172-038X | - |
dc.description.lastpage | 705 | en_US |
dc.identifier.issue | 9 | - |
dc.description.firstpage | 700 | en_US |
dc.relation.volume | 1 | en_US |
dc.investigacion | Ingeniería y Arquitectura | en_US |
dc.type2 | Artículo | en_US |
dc.utils.revision | Sí | en_US |
dc.date.coverdate | Mayo 2011 | en_US |
dc.identifier.ulpgc | Sí | en_US |
item.grantfulltext | open | - |
item.fulltext | Con texto completo | - |
crisitem.author.dept | GIR SIANI: Inteligencia Artificial, Redes Neuronales, Aprendizaje Automático e Ingeniería de Datos | - |
crisitem.author.dept | IU Sistemas Inteligentes y Aplicaciones Numéricas | - |
crisitem.author.dept | Departamento de Informática y Sistemas | - |
crisitem.author.orcid | 0000-0001-9717-8048 | - |
crisitem.author.parentorg | IU Sistemas Inteligentes y Aplicaciones Numéricas | - |
crisitem.author.fullName | Hernández Tejera, Francisco Mario | - |
Colección: | Artículos |
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