An investigation of indoor thermal environments and thermal comfort in naturally ventilated educational buildings

Indoor thermal conditions are essential in educational buildings. The health and well-being of students can be affected by a poor thermal environment, which also has a clear impact on building energy consumption. In this context, the indoor thermal environment in naturally ventilated university clas...

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Detalhes bibliográficos
Autores: Hoz Torres, María Luisa de la, Aguilar Aguilera, Antonio Jesús, Ruiz Padillo, Diego Pablo, Martínez Aires, María Dolores
Tipo de documento: artigo
Data de publicação:2024
País:España
Recursos:Consejo General de la Arquitectura Técnica de España (CGATE)
Repositório:RIARTE
OAI Identifier:oai:www.riarte.es:20.500.12251/3729
Acesso em linha:http://hdl.handle.net/20.500.12251/3729
https://doi.org/10.1016/j.jobe.2024.108677
Access Level:Acceso aberto
Palavra-chave:Centros educativos
Universidad
Cuestionario
Confort térmico
Sensorización
Ventilación natural
Calidad percibida
Algoritmos
Redes neuronales
3305.26 Edificios Públicos
1203.06 Sistemas Automatizados de Control d
3308.04 Ingeniería de la Contaminación
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spelling An investigation of indoor thermal environments and thermal comfort in naturally ventilated educational buildingsHoz Torres, María Luisa de laAguilar Aguilera, Antonio JesúsRuiz Padillo, Diego PabloMartínez Aires, María DoloresCentros educativosUniversidadCuestionarioConfort térmicoSensorizaciónVentilación naturalCalidad percibidaAlgoritmosRedes neuronales3305.26 Edificios Públicos1203.06 Sistemas Automatizados de Control d3308.04 Ingeniería de la ContaminaciónIndoor thermal conditions are essential in educational buildings. The health and well-being of students can be affected by a poor thermal environment, which also has a clear impact on building energy consumption. In this context, the indoor thermal environment in naturally ventilated university classrooms is explored in this study during a complete academic year. A monitoring campaign and a questionnaire survey were conducted simultaneously in higher education buildings in Spain. A total of 2115 sets of data were collected. Thermal sensation prediction indices (predicted mean vote, extended predicted mean vote and adaptive predicted mean vote) were applied to evaluate student’s thermal perception and their prediction accuracy was assessed. Additionally, two machine-learning models, based on Artificial neural network (ANN) and random forest (RF) algorithms, were formulated to predict occupants’ thermal sensation. The obtained results evidenced that the proposed ANN and RF models outperform traditional indices. Finally, it is also proposed an adaptive thermal comfort model. The results obtained suggest that students have a greater adaptive capacity to changes in environmental conditions than suggested by the ASHRAE-55 adaptive model and that they preferred an environment with lower temperatures than those suggested by the EN-16798 adaptive model.ELSEVIER2024info:eu-repo/semantics/articlehttp://hdl.handle.net/20.500.12251/3729https://doi.org/10.1016/j.jobe.2024.108677reponame:RIARTEinstname:Consejo General de la Arquitectura Técnica de España (CGATE)Ingléshttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:www.riarte.es:20.500.12251/37292026-06-02T12:44:41Z
dc.title.none.fl_str_mv An investigation of indoor thermal environments and thermal comfort in naturally ventilated educational buildings
title An investigation of indoor thermal environments and thermal comfort in naturally ventilated educational buildings
spellingShingle An investigation of indoor thermal environments and thermal comfort in naturally ventilated educational buildings
Hoz Torres, María Luisa de la
Centros educativos
Universidad
Cuestionario
Confort térmico
Sensorización
Ventilación natural
Calidad percibida
Algoritmos
Redes neuronales
3305.26 Edificios Públicos
1203.06 Sistemas Automatizados de Control d
3308.04 Ingeniería de la Contaminación
title_short An investigation of indoor thermal environments and thermal comfort in naturally ventilated educational buildings
title_full An investigation of indoor thermal environments and thermal comfort in naturally ventilated educational buildings
title_fullStr An investigation of indoor thermal environments and thermal comfort in naturally ventilated educational buildings
title_full_unstemmed An investigation of indoor thermal environments and thermal comfort in naturally ventilated educational buildings
title_sort An investigation of indoor thermal environments and thermal comfort in naturally ventilated educational buildings
dc.creator.none.fl_str_mv Hoz Torres, María Luisa de la
Aguilar Aguilera, Antonio Jesús
Ruiz Padillo, Diego Pablo
Martínez Aires, María Dolores
author Hoz Torres, María Luisa de la
author_facet Hoz Torres, María Luisa de la
Aguilar Aguilera, Antonio Jesús
Ruiz Padillo, Diego Pablo
Martínez Aires, María Dolores
author_role author
author2 Aguilar Aguilera, Antonio Jesús
Ruiz Padillo, Diego Pablo
Martínez Aires, María Dolores
author2_role author
author
author
dc.subject.none.fl_str_mv Centros educativos
Universidad
Cuestionario
Confort térmico
Sensorización
Ventilación natural
Calidad percibida
Algoritmos
Redes neuronales
3305.26 Edificios Públicos
1203.06 Sistemas Automatizados de Control d
3308.04 Ingeniería de la Contaminación
topic Centros educativos
Universidad
Cuestionario
Confort térmico
Sensorización
Ventilación natural
Calidad percibida
Algoritmos
Redes neuronales
3305.26 Edificios Públicos
1203.06 Sistemas Automatizados de Control d
3308.04 Ingeniería de la Contaminación
description Indoor thermal conditions are essential in educational buildings. The health and well-being of students can be affected by a poor thermal environment, which also has a clear impact on building energy consumption. In this context, the indoor thermal environment in naturally ventilated university classrooms is explored in this study during a complete academic year. A monitoring campaign and a questionnaire survey were conducted simultaneously in higher education buildings in Spain. A total of 2115 sets of data were collected. Thermal sensation prediction indices (predicted mean vote, extended predicted mean vote and adaptive predicted mean vote) were applied to evaluate student’s thermal perception and their prediction accuracy was assessed. Additionally, two machine-learning models, based on Artificial neural network (ANN) and random forest (RF) algorithms, were formulated to predict occupants’ thermal sensation. The obtained results evidenced that the proposed ANN and RF models outperform traditional indices. Finally, it is also proposed an adaptive thermal comfort model. The results obtained suggest that students have a greater adaptive capacity to changes in environmental conditions than suggested by the ASHRAE-55 adaptive model and that they preferred an environment with lower temperatures than those suggested by the EN-16798 adaptive model.
publishDate 2024
dc.date.none.fl_str_mv 2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.12251/3729
https://doi.org/10.1016/j.jobe.2024.108677
url http://hdl.handle.net/20.500.12251/3729
https://doi.org/10.1016/j.jobe.2024.108677
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv ELSEVIER
publisher.none.fl_str_mv ELSEVIER
dc.source.none.fl_str_mv reponame:RIARTE
instname:Consejo General de la Arquitectura Técnica de España (CGATE)
instname_str Consejo General de la Arquitectura Técnica de España (CGATE)
reponame_str RIARTE
collection RIARTE
repository.name.fl_str_mv
repository.mail.fl_str_mv
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