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

David Bienvenido-Huertas, Juan Moyano, Carlos E. Rodríguez-Jiménez and David Marín

Applied Energy, 2019, vol. 233-234, 14 pages

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.

Keywords: Thermal transmittance; Thermometric method; Thermal mass factors; Artificial neural network; Multilayer perceptron (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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DOI: 10.1016/j.apenergy.2018.10.052

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