Experimental characterisation of the periodic thermal properties of walls using artificial intelligence

David Bienvenido-Huertas, Carlos Rubio-Bellido, Jaime Solís-Guzmán and Miguel José Oliveira

Energy, 2020, vol. 203, issue C

Abstract: 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.

Keywords: Periodic thermal transmittance; Energy demand; ISO 13786; Multilayer perceptron; Random forests; In-situ (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544220309786
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:203:y:2020:i:c:s0360544220309786

DOI: 10.1016/j.energy.2020.117871

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().