Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/55460
Campo DC Valoridioma
dc.contributor.authorVelázquez Medina, Sergioen_US
dc.contributor.authorCarta González, José Antonioen_US
dc.contributor.authorPortero Ajenjo, Ulisesen_US
dc.date.accessioned2019-05-21T16:14:46Z-
dc.date.available2019-05-21T16:14:46Z-
dc.date.issued2019en_US
dc.identifier.issn1076-2787en_US
dc.identifier.otherWoS-
dc.identifier.urihttp://hdl.handle.net/10553/55460-
dc.description.abstractImproving the estimation of the power output of a wind farm enables greater integration of this type of energy source in electrical systems. The development of accurate models that represent the real operation of a wind farm is one way to attain this objective. A wind farm power curve model is proposed in this paper which is developed using artificial neural networks, and a study is undertaken of the influence on model performance when parameters such as the meteorological conditions (wind speed and direction) of areas other than the wind farm location are added as signals of the input layer of the neural network. Using such information could be of interest, either to study possible improvements that could be obtained in the performance of the original model, which uses exclusively the meteorological conditions of the area where the wind farm is located, or simply because no reliable meteorological data for the area of the wind farm are available. In the study developed it is deduced that the incorporation of meteorological data from an additional weather station other than that of the wind farm site can improve by up to 17.6% the performance of the original model.en_US
dc.languageengen_US
dc.relation.ispartofComplexityen_US
dc.sourceComplexity [ISSN 1076-2787], v. 2019en_US
dc.subject332202 Generación de energíaen_US
dc.subject.otherWakeen_US
dc.subject.otherOptimizationen_US
dc.subject.otherEnergyen_US
dc.titlePerformance sensitivity of a wind farm power curve model to different signals of the input layer of ANNs: Case studies in the Canary Islandsen_US
dc.typeinfo:eu-repo/semantics/articleen_US
dc.typeArticleen_US
dc.identifier.doi10.1155/2019/2869149en_US
dc.identifier.scopus85063886489-
dc.identifier.isi000464714000001-
dc.contributor.orcid#NODATA#-
dc.contributor.orcid#NODATA#-
dc.contributor.orcid#NODATA#-
dc.contributor.authorscopusid24336784400-
dc.contributor.authorscopusid7003652043-
dc.contributor.authorscopusid57208134380-
dc.identifier.eissn1099-0526-
dc.description.lastpage11en_US
dc.description.firstpage1en_US
dc.relation.volume2019en_US
dc.investigacionIngeniería y Arquitecturaen_US
dc.type2Artículoen_US
dc.contributor.daisngid8871675-
dc.contributor.daisngid1198474-
dc.contributor.daisngid29984970-
dc.description.notasThis research has been cofunded by ERDF funds, INTERREG MAC 2014-2020 programme, within the ENERMAC project (MAC/1.1a/117).en_US
dc.description.numberofpages11en_US
dc.utils.revisionen_US
dc.contributor.wosstandardWOS:Medina, SV-
dc.contributor.wosstandardWOS:Carta, JA-
dc.contributor.wosstandardWOS:Ajenjo, UP-
dc.date.coverdate2019en_US
dc.identifier.ulpgces
dc.description.sjr0,507
dc.description.jcr2,462
dc.description.sjrqQ1
dc.description.jcrqQ2
dc.description.scieSCIE
item.fulltextCon texto completo-
item.grantfulltextopen-
crisitem.author.deptGIR Group for the Research on Renewable Energy Systems-
crisitem.author.deptDepartamento de Ingeniería Electrónica y Automática-
crisitem.author.deptGIR Group for the Research on Renewable Energy Systems-
crisitem.author.deptDepartamento de Ingeniería Mecánica-
crisitem.author.orcid0000-0002-0392-6605-
crisitem.author.orcid0000-0003-1379-0075-
crisitem.author.parentorgDepartamento de Ingeniería Mecánica-
crisitem.author.parentorgDepartamento de Ingeniería Mecánica-
crisitem.author.fullNameVelázquez Medina, Sergio Leandro-
crisitem.author.fullNameCarta González, José Antonio-
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