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dc.contributor.authorMartínez Comesaña, Miguel 
dc.contributor.authorFebrero Garrido, Lara 
dc.contributor.authorGranada Álvarez, Enrique 
dc.contributor.authorMartínez Torres, Javier
dc.contributor.authorMartinez Mariño, Sandra 
dc.date.accessioned2021-03-25T12:03:43Z
dc.date.available2021-03-25T12:03:43Z
dc.date.issued2020-12-16
dc.identifier.citationApplied Sciences, 10(24): 8968 (2020)spa
dc.identifier.issn20763417
dc.identifier.urihttp://hdl.handle.net/11093/1897
dc.description.abstractThe Heat Loss Coefficient (HLC) characterizes the envelope efficiency of a building under in-use conditions, and it represents one of the main causes of the performance gap between the building design and its real operation. Accurate estimations of the HLC contribute to optimizing the energy consumption of a building. In this context, the application of black-box models in building energy analysis has been consolidated in recent years. The aim of this paper is to estimate the HLC of an existing building through the prediction of building thermal demands using a methodology based on Machine Learning (ML) models. Specifically, three different ML methods are applied to a public library in the northwest of Spain and compared; eXtreme Gradient Boosting (XGBoost), Support Vector Regression (SVR) and Multi-Layer Perceptron (MLP) neural network. Furthermore, the accuracy of the results is measured, on the one hand, using both CV(RMSE) and Normalized Mean Biased Error (NMBE), as advised by AHSRAE, for thermal demand predictions and, on the other, an absolute error for HLC estimations. The main novelty of this paper lies in the estimation of the HLC of a building considering thermal demand predictions reducing the requirement for monitoring. The results show that the most accurate model is capable of estimating the HLC of the building with an absolute error between 4 and 6%.spa
dc.description.sponsorshipMinisterio de Ciencia, Innovación y Universidades | Ref. RTI2018-096296-B-C21spa
dc.language.isoengspa
dc.publisherApplied Sciencesspa
dc.rightsCreative Commons Attribution (CC BY) license
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.titleHeat loss coefficient estimation applied to existing buildings through machine learning modelsspa
dc.typearticlespa
dc.rights.accessRightsopenAccessspa
dc.identifier.doi10.3390/app10248968
dc.identifier.editorhttps://www.mdpi.com/2076-3417/10/24/8968spa
dc.publisher.departamentoEnxeñaría mecánica, máquinas e motores térmicos e fluídosspa
dc.publisher.departamentoMatemática aplicada Ispa
dc.publisher.grupoinvestigacionGTE (Grupo de Tecnoloxía Enerxética)spa
dc.publisher.grupoinvestigacionExplotación de Minasspa
dc.subject.unesco3305.90 Transmisión de Calor en la Edificaciónspa
dc.subject.unesco3305.26 Edificios Públicosspa
dc.subject.unesco1203.26 Simulaciónspa
dc.date.updated2021-03-25T11:26:17Z


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