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.
Highlights
Most recent studies in building physics focus on energy savings and/or thermal comfort
Taking into account that a considerable amount of energy attributed to buildings is used to provide thermal comfort, and that in modern societies people spend over 90% of their time indoors [1], buildings became responsible for a considerable amount of energy demand worldwide [2,3]
The indoor latent heat flux is small when compared to the sensible flux, the the daytime, when the adsorbed moisture at nighttime is released, decreasing the concrete tile the mass playan animportant importantrole role heat exchanged at external the external surface, mainly, masstransport transport can can play inin thethe heat exchanged at the surface, mainly, in temperature due to the outward evaporation
Summary
Most recent studies in building physics focus on energy savings and/or thermal comfort. The main is to perform a artificial intelligence method in predict theonhygrothermal behavior of idea building roofs, focusing nonlinear system identification by using data obtained from the results of the numerical model. The main idea is to perform a nonlinear this manner, the main objectives are to reduce computational costs and to provide consistent system identification by using data obtained from the results of the numerical model. In the application presented in this work, both sensible this work, was proposed, evaluated, and compared to the classical version of SVMs [36,37] for a and latent heat flows, vapor flow and mould growth risk on only, concrete tiles, which is are constantly regression/identification task.
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