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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Autores: Bienvenido Huertas, José David, Rubio Bellido, Carlos, Solís-Guzmán, Jaime, Oliveira, Miguel José
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2020
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/103384
Acceso en línea:https://hdl.handle.net/11441/103384
https://doi.org/10.1016/j.energy.2020.117871
Access Level:acceso abierto
Palabra clave:Periodic thermal transmittance
Energy demand
ISO 13786
Multilayer perceptron
Random forests
In-situ
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spelling Experimental characterisation of the periodic thermal properties of walls using artificial intelligenceBienvenido Huertas, José DavidRubio Bellido, CarlosSolís-Guzmán, JaimeOliveira, Miguel JoséPeriodic thermal transmittanceEnergy demandISO 13786Multilayer perceptronRandom forestsIn-situThe 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.ElsevierUrbanística y Ordenación del Territorio2020info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/103384https://doi.org/10.1016/j.energy.2020.117871reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésEnergy, 203, 1-16.https://doi.org/10.1016/j.energy.2020.117871info:eu-repo/semantics/openAccessoai:idus.us.es:11441/1033842026-06-17T12:51:07Z
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, José David
Periodic thermal transmittance
Energy demand
ISO 13786
Multilayer perceptron
Random forests
In-situ
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, José David
Rubio Bellido, Carlos
Solís-Guzmán, Jaime
Oliveira, Miguel José
author Bienvenido Huertas, José David
author_facet Bienvenido Huertas, José 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.contributor.none.fl_str_mv Urbanística y Ordenación del Territorio
dc.subject.none.fl_str_mv Periodic thermal transmittance
Energy demand
ISO 13786
Multilayer perceptron
Random forests
In-situ
topic Periodic thermal transmittance
Energy demand
ISO 13786
Multilayer perceptron
Random forests
In-situ
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.
publishDate 2020
dc.date.none.fl_str_mv 2020
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/103384
https://doi.org/10.1016/j.energy.2020.117871
url https://hdl.handle.net/11441/103384
https://doi.org/10.1016/j.energy.2020.117871
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Energy, 203, 1-16.
https://doi.org/10.1016/j.energy.2020.117871
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,301603

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