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Applying an artificial neural network to assess thermal transmittance in walls by means of the thermometric method

Identifiers
URI: http://hdl.handle.net/20.500.12251/1579
ISSN: 3062619
DOI: 10.1016/j.apenergy.2018.10.052
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Author
Bienvenido Huertas, David; Moyano Campos, Juan José; Rodríguez Jiménez, Carlos Eugenio; Marín García, David
Date
2019
Subject/s

Comportamiento térmico

Transmitancia térmica

Fachadas

Perceptrón multicapa

Modelo de evaluación

Propiedades térmicas

Unesco Subject/s

3305.14 Viviendas

3305.90 Transmisión de Calor en la Edificación

3311.16 Instrumentos de Medida de la Temperatura

Abstract

Most of the existing building stock has a deficient energy behaviour. The thermal transmittance of façades is among those aspects which most affect this situation. In this paper, the calculation procedure with correction for storage effects from ISO 9869-1 was applied to the thermometric method to determine the U-value. Due to the need for determining the number and type of layers that compose the wall to apply the calculation, a multilayer perceptron has been developed to estimate the U-value. From the different model configurations suggested, the most adequate architecture was the one with 14 nodes in the hidden layer without making transformations in the input variables. Valid results have been obtained by the multilayer perceptron for the case studies analysed from different building periods, with deviations lower than 20% between the measured value and the expected one, varying the test duration according to the thermal resistance of the wall and the temperature variations. Furthermore, it is not necessary to carry out a data post-processing for the model, so this fact simplifies and hastens the calculation procedure. © 2018 Elsevier Ltd

Most of the existing building stock has a deficient energy behaviour. The thermal transmittance of façades is among those aspects which most affect this situation. In this paper, the calculation procedure with correction for storage effects from ISO 9869-1 was applied to the thermometric method to determine the U-value. Due to the need for determining the number and type of layers that compose the wall to apply the calculation, a multilayer perceptron has been developed to estimate the U-value. From the different model configurations suggested, the most adequate architecture was the one with 14 nodes in the hidden layer without making transformations in the input variables. Valid results have been obtained by the multilayer perceptron for the case studies analysed from different building periods, with deviations lower than 20% between the measured value and the expected one, varying the test duration according to the thermal resistance of the wall and the temperature variations. Furthermore, it is not necessary to carry out a data post-processing for the model, so this fact simplifies and hastens the calculation procedure. © 2018 Elsevier Ltd

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