Hybrid model to improve wind energy prediction considering data granularity

The growth of wind energy generation as a renewable source in the transition to sustainable energy poses significant challenges in ensuring reliable production forecasting due to the intermittent nature of wind resources. The implementation of advanced forecasting models has become a priority to opt...

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Detalhes bibliográficos
Autores: Ricardo de la Rosa, Leslie, García Pérez, Lía, Santos Peñas, Matilde, Gómez, Alejandro
Tipo de documento: artigo
Data de publicação:2025
País:España
Recursos:Universidad Complutense de Madrid (UCM)
Repositório:Docta Complutense
Idioma:inglês
OAI Identifier:oai:docta.ucm.es:20.500.14352/126252
Acesso em linha:https://hdl.handle.net/20.500.14352/126252
Access Level:Acceso aberto
Palavra-chave:Wind energy forecasting
Renewable energy
Machine learning
SARIMAX
GBoost
LSTM
Hybrid model
Inteligencia artificial (Informática)
1203.04 Inteligencia Artificial
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oai_identifier_str oai:docta.ucm.es:20.500.14352/126252
network_acronym_str ES
network_name_str España
repository_id_str
spelling Hybrid model to improve wind energy prediction considering data granularityRicardo de la Rosa, LeslieGarcía Pérez, LíaSantos Peñas, MatildeGómez, AlejandroWind energy forecastingRenewable energyMachine learningSARIMAXGBoostLSTMHybrid modelInteligencia artificial (Informática)1203.04 Inteligencia ArtificialThe growth of wind energy generation as a renewable source in the transition to sustainable energy poses significant challenges in ensuring reliable production forecasting due to the intermittent nature of wind resources. The implementation of advanced forecasting models has become a priority to optimize its integration into the power grid and ensure the stability of the energy supply. This study focuses on improving wind energy predictions through the use of advanced machine learning techniques. The methodology includes a detailed analysis of different forecasting time horizons, sampling rates, and exogenous variable configurations, comparing traditional models such as SARIMAX, XGBoost, and LSTM neural networks with hybrid approaches. Furthermore, performance metrics such as R2, MAE, RMSE and MAPE are evaluated to assess the accuracy and reliability of the proposed models. The results demonstrate that the selection of the optimal model depends on the forecasting horizon, data granularity, and available resources, maximizing precision and efficiency for each scenario.ElsevierUniversidad Complutense de Madrid20252025-12-1420252025-12-14journal articlehttp://purl.org/coar/resource_type/c_6501info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/20.500.14352/126252reponame:Docta Complutenseinstname:Universidad Complutense de Madrid (UCM)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:docta.ucm.es:20.500.14352/1262522026-06-02T12:44:21Z
dc.title.none.fl_str_mv Hybrid model to improve wind energy prediction considering data granularity
title Hybrid model to improve wind energy prediction considering data granularity
spellingShingle Hybrid model to improve wind energy prediction considering data granularity
Ricardo de la Rosa, Leslie
Wind energy forecasting
Renewable energy
Machine learning
SARIMAX
GBoost
LSTM
Hybrid model
Inteligencia artificial (Informática)
1203.04 Inteligencia Artificial
title_short Hybrid model to improve wind energy prediction considering data granularity
title_full Hybrid model to improve wind energy prediction considering data granularity
title_fullStr Hybrid model to improve wind energy prediction considering data granularity
title_full_unstemmed Hybrid model to improve wind energy prediction considering data granularity
title_sort Hybrid model to improve wind energy prediction considering data granularity
dc.creator.none.fl_str_mv Ricardo de la Rosa, Leslie
García Pérez, Lía
Santos Peñas, Matilde
Gómez, Alejandro
author Ricardo de la Rosa, Leslie
author_facet Ricardo de la Rosa, Leslie
García Pérez, Lía
Santos Peñas, Matilde
Gómez, Alejandro
author_role author
author2 García Pérez, Lía
Santos Peñas, Matilde
Gómez, Alejandro
author2_role author
author
author
dc.contributor.none.fl_str_mv Universidad Complutense de Madrid
dc.subject.none.fl_str_mv Wind energy forecasting
Renewable energy
Machine learning
SARIMAX
GBoost
LSTM
Hybrid model
Inteligencia artificial (Informática)
1203.04 Inteligencia Artificial
topic Wind energy forecasting
Renewable energy
Machine learning
SARIMAX
GBoost
LSTM
Hybrid model
Inteligencia artificial (Informática)
1203.04 Inteligencia Artificial
description The growth of wind energy generation as a renewable source in the transition to sustainable energy poses significant challenges in ensuring reliable production forecasting due to the intermittent nature of wind resources. The implementation of advanced forecasting models has become a priority to optimize its integration into the power grid and ensure the stability of the energy supply. This study focuses on improving wind energy predictions through the use of advanced machine learning techniques. The methodology includes a detailed analysis of different forecasting time horizons, sampling rates, and exogenous variable configurations, comparing traditional models such as SARIMAX, XGBoost, and LSTM neural networks with hybrid approaches. Furthermore, performance metrics such as R2, MAE, RMSE and MAPE are evaluated to assess the accuracy and reliability of the proposed models. The results demonstrate that the selection of the optimal model depends on the forecasting horizon, data granularity, and available resources, maximizing precision and efficiency for each scenario.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025-12-14
2025
2025-12-14
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.14352/126252
url https://hdl.handle.net/20.500.14352/126252
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.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Docta Complutense
instname:Universidad Complutense de Madrid (UCM)
instname_str Universidad Complutense de Madrid (UCM)
reponame_str Docta Complutense
collection Docta Complutense
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,228081

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