A hierarchical classification/regression algorithm for improving extreme wind speed events prediction

A novel method for prediction of the extreme wind speed events based on a Hierarchical Classification/Regression (HCR) approach is proposed. The idea is to improve the prediction skills of different Machine Learning approaches on extreme wind speed events, while preserving the prediction performance...

Descripción completa

Detalles Bibliográficos
Autores: Pérez Aracil, Jorge|||0000-0002-4456-9886, Salcedo Sanz, Sancho|||0000-0002-4048-1676, Peláez Rodríguez, César|||0000-0003-1260-8112, Dusân, Fister, Prieto Godino, Luis, Deo, Ravinesh C.
Tipo de recurso: artículo
Fecha de publicación:2022
País:España
Institución:Universidad de Alcalá (UAH)
Repositorio:e_Buah Biblioteca Digital Universidad de Alcalá
Idioma:inglés
OAI Identifier:oai:ebuah.uah.es:10017/64757
Acceso en línea:http://hdl.handle.net/10017/64757
https://dx.doi.org/10.1016/j.renene.2022.11.042
Access Level:acceso abierto
Palabra clave:Wind speed extremes
Wind extremes prediction
Hierarchical classification/regression schemes
Wind energy
Machine learning
Energías Renovables/Energías Alternativas
Alternative energies
id ES_045c4c3a6feb322d164bc23e98611fdd
oai_identifier_str oai:ebuah.uah.es:10017/64757
network_acronym_str ES
network_name_str España
repository_id_str
spelling A hierarchical classification/regression algorithm for improving extreme wind speed events predictionPérez Aracil, Jorge|||0000-0002-4456-9886Salcedo Sanz, Sancho|||0000-0002-4048-1676Peláez Rodríguez, César|||0000-0003-1260-8112Dusân, FisterPrieto Godino, LuisDeo, Ravinesh C.Wind speed extremesWind extremes predictionHierarchical classification/regression schemesWind energyMachine learningEnergías Renovables/Energías AlternativasAlternative energiesA novel method for prediction of the extreme wind speed events based on a Hierarchical Classification/Regression (HCR) approach is proposed. The idea is to improve the prediction skills of different Machine Learning approaches on extreme wind speed events, while preserving the prediction performance for steady events. The proposed HCR architecture rests on three distinctive levels: first, a data preprocessing level, where training data are divided into clusters and accordingly associated labels. At this point, balancing techniques are applied to increase the significance of clusters with poorly represented wind gusts data. At a second level of the architecture, the classification of each sample into the corresponding cluster is carried out. Finally, once we have determined the cluster a sample belongs to, the third level carries out the prediction of the wind speed value, by using the regression model associated with that particular cluster. The performance of the proposed HCR approach has been tested in a real database of hourly wind speed values in Spain, considering Reanalysis data as predictive variables. The results obtained have shown excellent prediction skill in the forecasting of extreme events, achieving a 96% extremes detection, while maintaining a reasonable performance in the non-extreme samples. The performance of the methods has also been assessed using forecast data (GFS) as predictors.20222022-12-01journal articlehttp://purl.org/coar/resource_type/c_6501NAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10017/64757https://dx.doi.org/10.1016/j.renene.2022.11.042reponame:e_Buah Biblioteca Digital Universidad de Alcaláinstname:Universidad de Alcalá (UAH)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:ebuah.uah.es:10017/647572026-06-18T11:13:07Z
dc.title.none.fl_str_mv A hierarchical classification/regression algorithm for improving extreme wind speed events prediction
title A hierarchical classification/regression algorithm for improving extreme wind speed events prediction
spellingShingle A hierarchical classification/regression algorithm for improving extreme wind speed events prediction
Pérez Aracil, Jorge|||0000-0002-4456-9886
Wind speed extremes
Wind extremes prediction
Hierarchical classification/regression schemes
Wind energy
Machine learning
Energías Renovables/Energías Alternativas
Alternative energies
title_short A hierarchical classification/regression algorithm for improving extreme wind speed events prediction
title_full A hierarchical classification/regression algorithm for improving extreme wind speed events prediction
title_fullStr A hierarchical classification/regression algorithm for improving extreme wind speed events prediction
title_full_unstemmed A hierarchical classification/regression algorithm for improving extreme wind speed events prediction
title_sort A hierarchical classification/regression algorithm for improving extreme wind speed events prediction
dc.creator.none.fl_str_mv Pérez Aracil, Jorge|||0000-0002-4456-9886
Salcedo Sanz, Sancho|||0000-0002-4048-1676
Peláez Rodríguez, César|||0000-0003-1260-8112
Dusân, Fister
Prieto Godino, Luis
Deo, Ravinesh C.
author Pérez Aracil, Jorge|||0000-0002-4456-9886
author_facet Pérez Aracil, Jorge|||0000-0002-4456-9886
Salcedo Sanz, Sancho|||0000-0002-4048-1676
Peláez Rodríguez, César|||0000-0003-1260-8112
Dusân, Fister
Prieto Godino, Luis
Deo, Ravinesh C.
author_role author
author2 Salcedo Sanz, Sancho|||0000-0002-4048-1676
Peláez Rodríguez, César|||0000-0003-1260-8112
Dusân, Fister
Prieto Godino, Luis
Deo, Ravinesh C.
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Wind speed extremes
Wind extremes prediction
Hierarchical classification/regression schemes
Wind energy
Machine learning
Energías Renovables/Energías Alternativas
Alternative energies
topic Wind speed extremes
Wind extremes prediction
Hierarchical classification/regression schemes
Wind energy
Machine learning
Energías Renovables/Energías Alternativas
Alternative energies
description A novel method for prediction of the extreme wind speed events based on a Hierarchical Classification/Regression (HCR) approach is proposed. The idea is to improve the prediction skills of different Machine Learning approaches on extreme wind speed events, while preserving the prediction performance for steady events. The proposed HCR architecture rests on three distinctive levels: first, a data preprocessing level, where training data are divided into clusters and accordingly associated labels. At this point, balancing techniques are applied to increase the significance of clusters with poorly represented wind gusts data. At a second level of the architecture, the classification of each sample into the corresponding cluster is carried out. Finally, once we have determined the cluster a sample belongs to, the third level carries out the prediction of the wind speed value, by using the regression model associated with that particular cluster. The performance of the proposed HCR approach has been tested in a real database of hourly wind speed values in Spain, considering Reanalysis data as predictive variables. The results obtained have shown excellent prediction skill in the forecasting of extreme events, achieving a 96% extremes detection, while maintaining a reasonable performance in the non-extreme samples. The performance of the methods has also been assessed using forecast data (GFS) as predictors.
publishDate 2022
dc.date.none.fl_str_mv 2022
2022-12-01
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/10017/64757
https://dx.doi.org/10.1016/j.renene.2022.11.042
url http://hdl.handle.net/10017/64757
https://dx.doi.org/10.1016/j.renene.2022.11.042
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-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/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-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/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
_version_ 1869402787631071232
score 15,198674

Similares en LA Referencia