Cluster-based demand prediction: a hybrid approach with grey wolf optimizer and multilayer perceptron network

[EN] The increasing complexity of microgrids in residential and industrial settings demands adaptive demand forecasting methods. This study introduces a novel approach that clusters household consumption data to identify distinct energy profiles, enabling precise modeling. A multilayer perceptron ne...

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Detalles Bibliográficos
Autores: Díaz-Bello, Dácil|||0000-0001-8416-9601, Vargas-Salgado, Carlos|||0000-0002-9259-8374, Montuori, Lina|||0000-0001-7574-7916, Alcázar-Ortega, Manuel|||0000-0001-5384-3931
Tipo de recurso: artículo
Fecha de publicación:2025
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/230553
Acceso en línea:https://riunet.upv.es/handle/10251/230553
Access Level:acceso abierto
Palabra clave:Demand forecasting
Energy
Clustering
Grey wolf optimizer
Random forest
Artificial neural network
07.- Asegurar el acceso a energías asequibles, fiables, sostenibles y modernas para todos
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repository_id_str
spelling Cluster-based demand prediction: a hybrid approach with grey wolf optimizer and multilayer perceptron networkDíaz-Bello, Dácil|||0000-0001-8416-9601Vargas-Salgado, Carlos|||0000-0002-9259-8374Montuori, Lina|||0000-0001-7574-7916Alcázar-Ortega, Manuel|||0000-0001-5384-3931Demand forecastingEnergyClusteringGrey wolf optimizerRandom forestArtificial neural network07.- Asegurar el acceso a energías asequibles, fiables, sostenibles y modernas para todos[EN] The increasing complexity of microgrids in residential and industrial settings demands adaptive demand forecasting methods. This study introduces a novel approach that clusters household consumption data to identify distinct energy profiles, enabling precise modeling. A multilayer perceptron network, optimized using the grey wolf optimizer, is tailored to these profiles, dynamically capturing unique consumption behaviors. Also, a random forest model also predicts future demand profiles based on factors like date, day type, and temperature, ensuring accurate profile assignment for each forecasting period. The proposed model achieves high accuracy, with normalized root-mean-squared error values of 0.04 for Case A and 0.05 for Case B, and mean absolute errors of 0.03¿0.05 kW, respectively. The total predicted demand closely matches the real values (7.37¿kWh vs. 7.34¿kWh for Case A and 7.31¿kWh vs. 7.56¿kWh for Case B). Prediction times are low (1.7428 s for Case A, 0.8215 s for Case B), and model training takes approximately 54 min. Compared to existing methods from state of the art, the proposed solution offers lower errors. The proposed combination of clustering, machine learning, and metaheuristic optimization establishes a strong and efficient framework for microgrid demand forecasting.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This research has been funded by the PURPOSED project (ref: PID2021-128822OB-I00), financed by the Spanish State Investigation Agency. In addition, one of the authors (D.D.B) was supported by the Ministry of Universities of Spain under the grant FPU21/00677.Springer-VerlagDepartamento de Termodinámica AplicadaDepartamento de Ingeniería EléctricaInstituto Universitario de Investigación de Ingeniería EnergéticaEscuela Técnica Superior de Ingeniería IndustrialMinisterio de UniversidadesAgencia Estatal de InvestigaciónUniversitat Politècnica de ValènciaRepositorio Institucional de la Universitat Politècnica de València Riunet20252025-08-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://riunet.upv.es/handle/10251/230553reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)InglésengAgencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023 PID2021-128822OB-I00 PLANIFICACION DE DISTRITOS URBANOS DE ENERGIA POSITIVAMinisterio de Universidades MIU FPU21%2F00677open accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento (by)http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/2305532026-06-13T07:49:27Z
dc.title.none.fl_str_mv Cluster-based demand prediction: a hybrid approach with grey wolf optimizer and multilayer perceptron network
title Cluster-based demand prediction: a hybrid approach with grey wolf optimizer and multilayer perceptron network
spellingShingle Cluster-based demand prediction: a hybrid approach with grey wolf optimizer and multilayer perceptron network
Díaz-Bello, Dácil|||0000-0001-8416-9601
Demand forecasting
Energy
Clustering
Grey wolf optimizer
Random forest
Artificial neural network
07.- Asegurar el acceso a energías asequibles, fiables, sostenibles y modernas para todos
title_short Cluster-based demand prediction: a hybrid approach with grey wolf optimizer and multilayer perceptron network
title_full Cluster-based demand prediction: a hybrid approach with grey wolf optimizer and multilayer perceptron network
title_fullStr Cluster-based demand prediction: a hybrid approach with grey wolf optimizer and multilayer perceptron network
title_full_unstemmed Cluster-based demand prediction: a hybrid approach with grey wolf optimizer and multilayer perceptron network
title_sort Cluster-based demand prediction: a hybrid approach with grey wolf optimizer and multilayer perceptron network
dc.creator.none.fl_str_mv Díaz-Bello, Dácil|||0000-0001-8416-9601
Vargas-Salgado, Carlos|||0000-0002-9259-8374
Montuori, Lina|||0000-0001-7574-7916
Alcázar-Ortega, Manuel|||0000-0001-5384-3931
author Díaz-Bello, Dácil|||0000-0001-8416-9601
author_facet Díaz-Bello, Dácil|||0000-0001-8416-9601
Vargas-Salgado, Carlos|||0000-0002-9259-8374
Montuori, Lina|||0000-0001-7574-7916
Alcázar-Ortega, Manuel|||0000-0001-5384-3931
author_role author
author2 Vargas-Salgado, Carlos|||0000-0002-9259-8374
Montuori, Lina|||0000-0001-7574-7916
Alcázar-Ortega, Manuel|||0000-0001-5384-3931
author2_role author
author
author
dc.contributor.none.fl_str_mv Departamento de Termodinámica Aplicada
Departamento de Ingeniería Eléctrica
Instituto Universitario de Investigación de Ingeniería Energética
Escuela Técnica Superior de Ingeniería Industrial
Ministerio de Universidades
Agencia Estatal de Investigación
Universitat Politècnica de València
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Demand forecasting
Energy
Clustering
Grey wolf optimizer
Random forest
Artificial neural network
07.- Asegurar el acceso a energías asequibles, fiables, sostenibles y modernas para todos
topic Demand forecasting
Energy
Clustering
Grey wolf optimizer
Random forest
Artificial neural network
07.- Asegurar el acceso a energías asequibles, fiables, sostenibles y modernas para todos
description [EN] The increasing complexity of microgrids in residential and industrial settings demands adaptive demand forecasting methods. This study introduces a novel approach that clusters household consumption data to identify distinct energy profiles, enabling precise modeling. A multilayer perceptron network, optimized using the grey wolf optimizer, is tailored to these profiles, dynamically capturing unique consumption behaviors. Also, a random forest model also predicts future demand profiles based on factors like date, day type, and temperature, ensuring accurate profile assignment for each forecasting period. The proposed model achieves high accuracy, with normalized root-mean-squared error values of 0.04 for Case A and 0.05 for Case B, and mean absolute errors of 0.03¿0.05 kW, respectively. The total predicted demand closely matches the real values (7.37¿kWh vs. 7.34¿kWh for Case A and 7.31¿kWh vs. 7.56¿kWh for Case B). Prediction times are low (1.7428 s for Case A, 0.8215 s for Case B), and model training takes approximately 54 min. Compared to existing methods from state of the art, the proposed solution offers lower errors. The proposed combination of clustering, machine learning, and metaheuristic optimization establishes a strong and efficient framework for microgrid demand forecasting.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025-08-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/230553
url https://riunet.upv.es/handle/10251/230553
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Agencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023 PID2021-128822OB-I00 PLANIFICACION DE DISTRITOS URBANOS DE ENERGIA POSITIVA
Ministerio de Universidades MIU FPU21%2F00677
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento (by)
http://creativecommons.org/licenses/by/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
Reconocimiento (by)
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Springer-Verlag
publisher.none.fl_str_mv Springer-Verlag
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.811543

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