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dc.contributor.authorBienvenido Huertas, David
dc.contributor.authorRubio Bellido, Carlos
dc.contributor.authorSolís Guzmán, Jaime
dc.contributor.authorOliveira, Miguel José
dc.date.accessioned2021-09-30T08:26:41Z
dc.date.available2021-09-30T08:26:41Z
dc.date.issued2020-07
dc.identifier.citationBienvenido Huertas, D., Rubio Bellido, C., Solís Guzmán, J. y Oliveira, M. J. (2020). Experimental characterisation of the periodic thermal properties of walls using artificial intelligence. Energy, 203, 117871. https://doi.org/10.1016/j.energy.2020.117871es
dc.identifier.issn03605442
dc.identifier.urihttp://hdl.handle.net/20.500.12251/1909
dc.description.abstractThe 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 Ltdes
dc.language.isoenges
dc.publisherElsevier Ltdes
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.titleExperimental characterisation of the periodic thermal properties of walls using artificial intelligencees
dc.typearticlees
dc.identifier.doi10.1016/j.energy.2020.117871
dc.identifier.urlhttps://doi.org/10.1016/j.energy.2020.117871es
dc.journal.titleEnergyes
dc.rights.accessRightsopenAccesses
dc.subject.keywordDemanda energéticaes
dc.subject.keywordTransmitancia térmicaes
dc.subject.keywordRendimiento energéticoes
dc.subject.keywordAlgoritmoses
dc.subject.keywordMuroses
dc.subject.keywordEnvolvente de edificioes
dc.subject.keywordInteligencia Artificiales
dc.subject.keywordMonitorización de edificioses
dc.subject.keywordFlujo térmicoes
dc.subject.keywordGradiente de temperaturaes
dc.subject.unesco2213.02 Física de la Transmisión del Calores
dc.subject.unesco3305.14 Viviendases
dc.subject.unesco2502.02 Climatología Aplicadaes
dc.subject.unesco3305.90 Transmisión de Calor en la Edificaciónes
dc.subject.unesco3311.16 Instrumentos de Medida de la Temperaturaes
dc.volume.number203es
dc.item.number117871es


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