Significant wave height and energy flux prediction for marine energy applications: A grouping genetic algorithm - Extreme Learning Machine approach

This paper proposes a novel hybrid approach for feature selection in two different relevant problems for marine energy applications: significant wave height (Hm0) and wave energy flux (P )nprediction. Specifically, a hybrid Grouping Genetic Algorithm &- Extreme Learning Machine approach (GGA-ELM...

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Autores: Cornejo Bueno, Laura María|||0000-0002-4126-8041, Nieto Borge, José Carlos|||0000-0002-3158-3822, García Díaz, María del Pilar|||0000-0002-5361-6947, Rodríguez Rodríguez, Germán, Salcedo Sanz, Sancho|||0000-0002-4048-1676
Formato: artículo
Fecha de publicación:2016
País:España
Recursos:Universidad de Alcalá (UAH)
Repositorio:e_Buah Biblioteca Digital Universidad de Alcalá
Idioma:inglés
OAI Identifier:oai:ebuah.uah.es:10017/64742
Acesso em linha:http://hdl.handle.net/10017/64742
https://dx.doi.org/10.1016/j.renene.2016.05.094
Access Level:acceso abierto
Palavra-chave:Wave energy flux
Marine energy
Significant wave height
Grouping genetic algorithm (GGA)
Extreme Learning Machines
Support vector machines
Energías Renovables/Energías Alternativas
Alternative energies
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oai_identifier_str oai:ebuah.uah.es:10017/64742
network_acronym_str ES
network_name_str España
repository_id_str
spelling Significant wave height and energy flux prediction for marine energy applications: A grouping genetic algorithm - Extreme Learning Machine approachCornejo Bueno, Laura María|||0000-0002-4126-8041Nieto Borge, José Carlos|||0000-0002-3158-3822García Díaz, María del Pilar|||0000-0002-5361-6947Rodríguez Rodríguez, GermánSalcedo Sanz, Sancho|||0000-0002-4048-1676Wave energy fluxMarine energySignificant wave heightGrouping genetic algorithm (GGA)Extreme Learning MachinesSupport vector machinesEnergías Renovables/Energías AlternativasAlternative energiesThis paper proposes a novel hybrid approach for feature selection in two different relevant problems for marine energy applications: significant wave height (Hm0) and wave energy flux (P )nprediction. Specifically, a hybrid Grouping Genetic Algorithm &- Extreme Learning Machine approach (GGA-ELM) is proposed, in such a way that the GGA searches for several subsets of features, and the ELM provides the fitness of the algorithm, by means of its accuracy on Hm0 or P prediction. Since the GGA was specifically created for problems involving a number of groups, the proposed algorithm may be used to evolve different groups of features in parallel, which may improve the performance of the predictions obtained. After the feature selection process with the GGA-ELM, the final results are given by an ELM and also by a Support Vector Machine, both working on the best GGA groups obtained. The performance of the proposed system has been tested in a real problem of Hm0 and P prediction at the Western coast of the USA, obtaining good results.20162016-11-30journal articlehttp://purl.org/coar/resource_type/c_6501NAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10017/64742https://dx.doi.org/10.1016/j.renene.2016.05.094reponame:e_Buah Biblioteca Digital Universidad de Alcaláinstname:Universidad de Alcalá (UAH)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-ShareAlike 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/openAccessoai:ebuah.uah.es:10017/647422026-06-18T11:13:07Z
dc.title.none.fl_str_mv Significant wave height and energy flux prediction for marine energy applications: A grouping genetic algorithm - Extreme Learning Machine approach
title Significant wave height and energy flux prediction for marine energy applications: A grouping genetic algorithm - Extreme Learning Machine approach
spellingShingle Significant wave height and energy flux prediction for marine energy applications: A grouping genetic algorithm - Extreme Learning Machine approach
Cornejo Bueno, Laura María|||0000-0002-4126-8041
Wave energy flux
Marine energy
Significant wave height
Grouping genetic algorithm (GGA)
Extreme Learning Machines
Support vector machines
Energías Renovables/Energías Alternativas
Alternative energies
title_short Significant wave height and energy flux prediction for marine energy applications: A grouping genetic algorithm - Extreme Learning Machine approach
title_full Significant wave height and energy flux prediction for marine energy applications: A grouping genetic algorithm - Extreme Learning Machine approach
title_fullStr Significant wave height and energy flux prediction for marine energy applications: A grouping genetic algorithm - Extreme Learning Machine approach
title_full_unstemmed Significant wave height and energy flux prediction for marine energy applications: A grouping genetic algorithm - Extreme Learning Machine approach
title_sort Significant wave height and energy flux prediction for marine energy applications: A grouping genetic algorithm - Extreme Learning Machine approach
dc.creator.none.fl_str_mv Cornejo Bueno, Laura María|||0000-0002-4126-8041
Nieto Borge, José Carlos|||0000-0002-3158-3822
García Díaz, María del Pilar|||0000-0002-5361-6947
Rodríguez Rodríguez, Germán
Salcedo Sanz, Sancho|||0000-0002-4048-1676
author Cornejo Bueno, Laura María|||0000-0002-4126-8041
author_facet Cornejo Bueno, Laura María|||0000-0002-4126-8041
Nieto Borge, José Carlos|||0000-0002-3158-3822
García Díaz, María del Pilar|||0000-0002-5361-6947
Rodríguez Rodríguez, Germán
Salcedo Sanz, Sancho|||0000-0002-4048-1676
author_role author
author2 Nieto Borge, José Carlos|||0000-0002-3158-3822
García Díaz, María del Pilar|||0000-0002-5361-6947
Rodríguez Rodríguez, Germán
Salcedo Sanz, Sancho|||0000-0002-4048-1676
author2_role author
author
author
author
dc.subject.none.fl_str_mv Wave energy flux
Marine energy
Significant wave height
Grouping genetic algorithm (GGA)
Extreme Learning Machines
Support vector machines
Energías Renovables/Energías Alternativas
Alternative energies
topic Wave energy flux
Marine energy
Significant wave height
Grouping genetic algorithm (GGA)
Extreme Learning Machines
Support vector machines
Energías Renovables/Energías Alternativas
Alternative energies
description This paper proposes a novel hybrid approach for feature selection in two different relevant problems for marine energy applications: significant wave height (Hm0) and wave energy flux (P )nprediction. Specifically, a hybrid Grouping Genetic Algorithm &- Extreme Learning Machine approach (GGA-ELM) is proposed, in such a way that the GGA searches for several subsets of features, and the ELM provides the fitness of the algorithm, by means of its accuracy on Hm0 or P prediction. Since the GGA was specifically created for problems involving a number of groups, the proposed algorithm may be used to evolve different groups of features in parallel, which may improve the performance of the predictions obtained. After the feature selection process with the GGA-ELM, the final results are given by an ELM and also by a Support Vector Machine, both working on the best GGA groups obtained. The performance of the proposed system has been tested in a real problem of Hm0 and P prediction at the Western coast of the USA, obtaining good results.
publishDate 2016
dc.date.none.fl_str_mv 2016
2016-11-30
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
NA
http://purl.org/coar/version/c_be7fb7dd8ff6fe43
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10017/64742
https://dx.doi.org/10.1016/j.renene.2016.05.094
url http://hdl.handle.net/10017/64742
https://dx.doi.org/10.1016/j.renene.2016.05.094
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-ShareAlike 4.0 International
http://creativecommons.org/licenses/by-nc-sa/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-ShareAlike 4.0 International
http://creativecommons.org/licenses/by-nc-sa/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:e_Buah Biblioteca Digital Universidad de Alcalá
instname:Universidad de Alcalá (UAH)
instname_str Universidad de Alcalá (UAH)
reponame_str e_Buah Biblioteca Digital Universidad de Alcalá
collection e_Buah Biblioteca Digital Universidad de Alcalá
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.812455

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