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Energies 2017, 10(8), 1093; doi:10.3390/en10081093

Hygrothermal Dynamic and Mould Growth Risk Predictions for Concrete Tiles by Using Least Squares Support Vector Machines

1
Industrial and Systems Engineering Graduate Program—PPGEPS, Polytechnic School—EP, Pontifical Catholic University of Parana—PUCPR, Rua Imaculada Conceição, 1155, Curitiba 80215-901, Brazil
2
Department of Mechanical Engineering, Federal Technological University of Parana—UTFPR, Av. Monteiro Lobato, Km 04, Ponta Grossa 84016-210, Brazil
3
Department of Electrical Engineering, Electrical Engineering Graduate Program—PPGEE, Federal University of Parana—UFPR, Av. Cel. Francisco H. dos Santos, 210, Curitiba 81531-970, Brazil
*
Author to whom correspondence should be addressed.
Received: 3 July 2017 / Revised: 17 July 2017 / Accepted: 24 July 2017 / Published: 26 July 2017
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Abstract

The hygrothermal analysis of roofs is relevant due to the large areas exposed to a wide range of weather conditions, these directly affecting the energy performance and thermal comfort of buildings. However, after a long life service, the solar absorptivity coatings of roofs can be altered by mould accumulation. Based on two well established mathematical models, one that adopts driving potentials to calculate temperature, moist air pressure and water vapor pressure gradients, and the other to estimate the mould growth risk on surfaces, this research introduces an approach to predict mould growth considering a reduced computational effort and simulation time. By adopting multiple MISO (Multiple-Input, Single-Output) Nonlinear AutoRegressive with eXogenous inputs (NARX) models, a machine learning technique known as Least Squares Support Vector Machines (LS-SVM), a maximum margin model based on structural risk minimization, was used to predict vapor flux, sensible heat flux, latent heat flux, and mould growth risk on roof surfaces. The proposed model was validated in terms of the Multiple Correlation Coefficient (R2R2R2), Mean Square Error (MSE) and Mean Absolute Error (MAE) performance indices considering as input the weather file from Curitiba city—Brazil, showing consistent precision when compared to the results of a validated numerical model. View Full-Text
Keywords: support vector machines; machine learning; system identification; concrete tiles; hygrothermal performance; mould growth support vector machines; machine learning; system identification; concrete tiles; hygrothermal performance; mould growth
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Freire, R.Z.; Santos, G.H.; Coelho, L.S. Hygrothermal Dynamic and Mould Growth Risk Predictions for Concrete Tiles by Using Least Squares Support Vector Machines. Energies 2017, 10, 1093.

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