Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/52556
DC FieldValueLanguage
dc.contributor.authorCuadra, L.en_US
dc.contributor.authorSalcedo-Sanz, S.en_US
dc.contributor.authorNieto-Borge, J.C.en_US
dc.contributor.authorAlexandre, E.en_US
dc.contributor.authorRodríguez, G.en_US
dc.date.accessioned2018-11-30T10:04:07Z-
dc.date.available2018-11-30T10:04:07Z-
dc.date.issued2016en_US
dc.identifier.issn1364-0321en_US
dc.identifier.urihttp://hdl.handle.net/10553/52556-
dc.description.abstractWind-generated wave energy is a renewable energy source that exhibits a huge potential for sustainable growth. The design and deployment of wave energy converters at a given location require the prediction of the amount of available wave energy flux. This and other wave parameters can be estimated by means of Computational Intelligence techniques (Neural, Fuzzy, and Evolutionary Computation). This paper reviews those used in wave energy applications, both in the resource estimation and in the design and control of wave energy converters. In particular, most of the applications of Neural Computation techniques, considered here in a broad sense, focus on the prediction of a variety of wave energy parameters by means of Multilayer Perceptrons and, at a lesser extent, by Support Vector Machines, and Extreme Learning Machines. Fuzzy Computation is also applied to estimate wave parameters and control floating wave energy converter. Evolutionary Computation algorithms are used to estimate parameters and design wave energy collectors. We complete this paper with a case study that illustrates, for the first time to the best of our knowledge, the potential of hybridizing a Coral Reefs Optimization algorithm with an Extreme Learning Machine to tackle the problem of significant wave height reconstruction.en_US
dc.languageengen_US
dc.relation.ispartofRenewable & Sustainable Energy Reviewsen_US
dc.sourceRenewable and Sustainable Energy Reviews[ISSN 1364-0321],v. 58, p. 1223-1246en_US
dc.subject251091 Recursos renovablesen_US
dc.subject2510 Oceanografíaen_US
dc.subject.otherComputational intelligence techniquesen_US
dc.subject.otherEnvironmental impacten_US
dc.subject.otherRenewable energyen_US
dc.subject.otherWave energyen_US
dc.subject.otherWave energy convertersen_US
dc.titleComputational intelligence in wave energy: Comprehensive review and case studyen_US
dc.typeinfo:eu-repo/semantics/reviewes
dc.typeArticlees
dc.identifier.doi10.1016/j.rser.2015.12.253
dc.identifier.scopus84954286170
dc.identifier.isi000371948100103
dc.contributor.authorscopusid6602769356
dc.contributor.authorscopusid12789591800
dc.contributor.authorscopusid55663344400
dc.contributor.authorscopusid22978729400
dc.contributor.authorscopusid7203006681
dc.description.lastpage1246-
dc.description.firstpage1223-
dc.relation.volume58-
dc.investigacionCienciasen_US
dc.type2Reseñaen_US
dc.contributor.daisngid1003734
dc.contributor.daisngid140631
dc.contributor.daisngid1867514
dc.contributor.daisngid1858932
dc.contributor.daisngid28190193
dc.contributor.wosstandardWOS:Cuadra, L
dc.contributor.wosstandardWOS:Salcedo-Sanz, S
dc.contributor.wosstandardWOS:Nieto-Borge, JC
dc.contributor.wosstandardWOS:Alexandre, E
dc.contributor.wosstandardWOS:Rodriguez, G
dc.date.coverdateMayo 2016
dc.identifier.ulpgces
dc.description.sjr3,051
dc.description.jcr8,05
dc.description.sjrqQ1
dc.description.jcrqQ1
dc.description.scieSCIE
item.grantfulltextnone-
item.fulltextSin texto completo-
crisitem.author.deptGIR IUNAT: Física marina y teledetección aplicada-
crisitem.author.deptIU de Estudios Ambientales y Recursos Naturales-
crisitem.author.deptDepartamento de Física-
crisitem.author.parentorgIU de Estudios Ambientales y Recursos Naturales-
crisitem.author.fullNameRodríguez Rodríguez, Germán Alejandro-
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