Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/43803
DC FieldValueLanguage
dc.contributor.authorCarta, José A.en_US
dc.contributor.authorVelázquez, Sergioen_US
dc.contributor.authorMatías, J. M.en_US
dc.date.accessioned2018-11-21T17:57:22Z-
dc.date.available2018-11-21T17:57:22Z-
dc.date.issued2011en_US
dc.identifier.issn0196-8904en_US
dc.identifier.urihttp://hdl.handle.net/10553/43803-
dc.description.abstractDue to the interannual variability of wind speed a feasibility analysis for the installation of a Wind Energy Conversion System at a particular site requires estimation of the long-term mean wind turbine energy output. A method is proposed in this paper which, based on probabilistic Bayesian networks (BNs), enables estimation of the long-term mean wind speed histogram for a site where few measurements of the wind resource are available. For this purpose, the proposed method allows the use of multiple reference stations with a long history of wind speed and wind direction measurements. That is to say, the model that is proposed in this paper is able to involve and make use of regional information about the wind resource. With the estimated long-term wind speed histogram and the power curve of a wind turbine it is possible to use the method of bins to determine the long-term mean energy output for that wind turbine. The intelligent system employed, the knowledgebase of which is a joint probability function of all the model variables, uses efficient calculation techniques for conditional probabilities to perform the reasoning. This enables automatic model learning and inference to be performed efficiently based on the available evidence. The proposed model is applied in this paper to wind speeds and wind directions recorded at four weather stations located in the Canary Islands (Spain). Ten years of mean hourly wind speed and direction data are available for these stations. One of the conclusions reached is that the BN with three reference stations gave fewer errors between the real and estimated long-term mean wind turbine energy output than when using two measure–correlate–predict algorithms which were evaluated and which use a linear regression between the candidate station and one reference.en_US
dc.languageengen_US
dc.publisher0196-8904
dc.relation.ispartofEnergy Conversion and Managementen_US
dc.sourceEnergy Conversion and Management [ISSN 0196-8904], v. 52 (2), p. 1137-1149en_US
dc.subject3322 Tecnología energéticaen_US
dc.subject1208 Probabilidaden_US
dc.subject.otherBayesian networken_US
dc.subject.otherMeasure–correlate–predicten_US
dc.subject.otherLong-term recordsen_US
dc.subject.otherMethod of binsen_US
dc.subject.otherWind speeden_US
dc.titleUse of Bayesian networks classifiers for long-term mean wind turbine energy output estimation at a potential wind energy conversion siteen_US
dc.typeinfo:eu-repo/semantics/Articlees
dc.typeArticlees
dc.identifier.doi10.1016/j.enconman.2010.09.008
dc.identifier.scopus78649320973-
dc.identifier.isi000285485100039
dc.contributor.authorscopusid7003652043-
dc.contributor.authorscopusid24336784400-
dc.contributor.authorscopusid8058596200-
dc.contributor.authorscopusid57211016243
dc.description.lastpage1149-
dc.description.firstpage1137-
dc.relation.volume52-
dc.investigacionIngeniería y Arquitecturaen_US
dc.type2Artículoen_US
dc.contributor.daisngid1198474
dc.contributor.daisngid8871675
dc.contributor.daisngid30320309
dc.utils.revisionen_US
dc.contributor.wosstandardWOS:Carta, JA
dc.contributor.wosstandardWOS:Velazquez, S
dc.contributor.wosstandardWOS:Matias, JM
dc.date.coverdateFebrero 2011
dc.identifier.ulpgces
dc.description.sjr1,292
dc.description.jcr2,216
dc.description.sjrqQ1
dc.description.jcrqQ1
dc.description.scieSCIE
item.grantfulltextopen-
item.fulltextCon texto completo-
crisitem.author.deptGIR Group for the Research on Renewable Energy Systems-
crisitem.author.deptDepartamento de Ingeniería Mecánica-
crisitem.author.deptGIR Group for the Research on Renewable Energy Systems-
crisitem.author.deptDepartamento de Ingeniería Electrónica y Automática-
crisitem.author.orcid0000-0003-1379-0075-
crisitem.author.orcid0000-0002-0392-6605-
crisitem.author.parentorgDepartamento de Ingeniería Mecánica-
crisitem.author.parentorgDepartamento de Ingeniería Mecánica-
crisitem.author.fullNameCarta González, José Antonio-
crisitem.author.fullNameVelázquez Medina, Sergio Leandro-
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