Experimental characterisation of the periodic thermal properties of walls using artificial intelligence

The energy performance of a building is affected by the periodic thermal properties of the walls, and reliable methods of characterising these are therefore required. However, the methods that are currently available involve theoretical calculations that make it difficult to assess the condition of...

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Detalhes bibliográficos
Autores: Bienvenido Huertas, David, Rubio Bellido, Carlos, Solís Guzmán, Jaime, Oliveira, Miguel José
Formato: artículo
Fecha de publicación:2020
País:España
Recursos:Consejo General de la Arquitectura Técnica de España (CGATE)
Repositorio:RIARTE
OAI Identifier:oai:www.riarte.es:20.500.12251/1909
Acesso em linha:http://hdl.handle.net/20.500.12251/1909
https://doi.org/10.1016/j.energy.2020.117871
Access Level:acceso abierto
Palavra-chave:Demanda energética
Transmitancia térmica
Rendimiento energético
Algoritmos
Muros
Envolvente de edificio
Inteligencia Artificial
Monitorización de edificios
Flujo térmico
Gradiente de temperatura
2213.02 Física de la Transmisión del Calor
3305.14 Viviendas
2502.02 Climatología Aplicada
3305.90 Transmisión de Calor en la Edificación
3311.16 Instrumentos de Medida de la Temperatura
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spelling Experimental characterisation of the periodic thermal properties of walls using artificial intelligenceBienvenido Huertas, DavidRubio Bellido, CarlosSolís Guzmán, JaimeOliveira, Miguel JoséDemanda energéticaTransmitancia térmicaRendimiento energéticoAlgoritmosMurosEnvolvente de edificioInteligencia ArtificialMonitorización de edificiosFlujo térmicoGradiente de temperatura2213.02 Física de la Transmisión del Calor3305.14 Viviendas2502.02 Climatología Aplicada3305.90 Transmisión de Calor en la Edificación3311.16 Instrumentos de Medida de la TemperaturaThe energy performance of a building is affected by the periodic thermal properties of the walls, and reliable methods of characterising these are therefore required. However, the methods that are currently available involve theoretical calculations that make it difficult to assess the condition of existing walls. In this study, the characterisation of the periodic thermal variables of walls using experimental measurements and methods as described in ISO 13786 was assessed. Two regression algorithms (multilayer perceptron [MLP] and random forest [RF]) and input variables obtained using two experimental methods (the heat flow meter and the thermometric method) were used. The methods gave accurate estimates, and better statistical parameter values were given by the RF models than the multilayer perceptron models. For all the periodic thermal variables, the percentage differences between the actual values and the estimated values given by the RF algorithm were low. The heat flow meter and the thermometric methods can both be used to characterise accurately the periodic thermal properties of walls using the RF algorithm. The variables specific to each method, including the wall thickness and the date of construction, affected the accuracies of the models most strongly. © 2020 Elsevier LtdElsevier Ltd2020info:eu-repo/semantics/articlehttp://hdl.handle.net/20.500.12251/1909https://doi.org/10.1016/j.energy.2020.117871reponame: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:20.500.12251/19092026-06-02T12:44:41Z
dc.title.none.fl_str_mv Experimental characterisation of the periodic thermal properties of walls using artificial intelligence
title Experimental characterisation of the periodic thermal properties of walls using artificial intelligence
spellingShingle Experimental characterisation of the periodic thermal properties of walls using artificial intelligence
Bienvenido Huertas, David
Demanda energética
Transmitancia térmica
Rendimiento energético
Algoritmos
Muros
Envolvente de edificio
Inteligencia Artificial
Monitorización de edificios
Flujo térmico
Gradiente de temperatura
2213.02 Física de la Transmisión del Calor
3305.14 Viviendas
2502.02 Climatología Aplicada
3305.90 Transmisión de Calor en la Edificación
3311.16 Instrumentos de Medida de la Temperatura
title_short Experimental characterisation of the periodic thermal properties of walls using artificial intelligence
title_full Experimental characterisation of the periodic thermal properties of walls using artificial intelligence
title_fullStr Experimental characterisation of the periodic thermal properties of walls using artificial intelligence
title_full_unstemmed Experimental characterisation of the periodic thermal properties of walls using artificial intelligence
title_sort Experimental characterisation of the periodic thermal properties of walls using artificial intelligence
dc.creator.none.fl_str_mv Bienvenido Huertas, David
Rubio Bellido, Carlos
Solís Guzmán, Jaime
Oliveira, Miguel José
author Bienvenido Huertas, David
author_facet Bienvenido Huertas, David
Rubio Bellido, Carlos
Solís Guzmán, Jaime
Oliveira, Miguel José
author_role author
author2 Rubio Bellido, Carlos
Solís Guzmán, Jaime
Oliveira, Miguel José
author2_role author
author
author
dc.subject.none.fl_str_mv Demanda energética
Transmitancia térmica
Rendimiento energético
Algoritmos
Muros
Envolvente de edificio
Inteligencia Artificial
Monitorización de edificios
Flujo térmico
Gradiente de temperatura
2213.02 Física de la Transmisión del Calor
3305.14 Viviendas
2502.02 Climatología Aplicada
3305.90 Transmisión de Calor en la Edificación
3311.16 Instrumentos de Medida de la Temperatura
topic Demanda energética
Transmitancia térmica
Rendimiento energético
Algoritmos
Muros
Envolvente de edificio
Inteligencia Artificial
Monitorización de edificios
Flujo térmico
Gradiente de temperatura
2213.02 Física de la Transmisión del Calor
3305.14 Viviendas
2502.02 Climatología Aplicada
3305.90 Transmisión de Calor en la Edificación
3311.16 Instrumentos de Medida de la Temperatura
description The energy performance of a building is affected by the periodic thermal properties of the walls, and reliable methods of characterising these are therefore required. However, the methods that are currently available involve theoretical calculations that make it difficult to assess the condition of existing walls. In this study, the characterisation of the periodic thermal variables of walls using experimental measurements and methods as described in ISO 13786 was assessed. Two regression algorithms (multilayer perceptron [MLP] and random forest [RF]) and input variables obtained using two experimental methods (the heat flow meter and the thermometric method) were used. The methods gave accurate estimates, and better statistical parameter values were given by the RF models than the multilayer perceptron models. For all the periodic thermal variables, the percentage differences between the actual values and the estimated values given by the RF algorithm were low. The heat flow meter and the thermometric methods can both be used to characterise accurately the periodic thermal properties of walls using the RF algorithm. The variables specific to each method, including the wall thickness and the date of construction, affected the accuracies of the models most strongly. © 2020 Elsevier Ltd
publishDate 2020
dc.date.none.fl_str_mv 2020
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.12251/1909
https://doi.org/10.1016/j.energy.2020.117871
url http://hdl.handle.net/20.500.12251/1909
https://doi.org/10.1016/j.energy.2020.117871
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 Ltd
publisher.none.fl_str_mv Elsevier Ltd
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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score 15,301603

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