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

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  • Roberto Zanetti Freire

    (Industrial and Systems Engineering Graduate Program—PPGEPS, Polytechnic School—EP, Pontifical Catholic University of Parana—PUCPR, Rua Imaculada Conceição, 1155, Curitiba 80215-901, Brazil)

  • Gerson Henrique dos Santos

    (Department of Mechanical Engineering, Federal Technological University of Parana—UTFPR, Av. Monteiro Lobato, Km 04, Ponta Grossa 84016-210, Brazil)

  • Leandro dos Santos Coelho

    (Industrial and Systems Engineering Graduate Program—PPGEPS, Polytechnic School—EP, Pontifical Catholic University of Parana—PUCPR, Rua Imaculada Conceição, 1155, Curitiba 80215-901, Brazil
    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)

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.

Suggested Citation

  • Roberto Zanetti Freire & Gerson Henrique dos Santos & Leandro dos Santos Coelho, 2017. "Hygrothermal Dynamic and Mould Growth Risk Predictions for Concrete Tiles by Using Least Squares Support Vector Machines," Energies, MDPI, vol. 10(8), pages 1-16, July.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:8:p:1093-:d:105884
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    1. Jin Woo Moon & Min Hee Chung & Hayub Song & Se-Young Lee, 2016. "Performance of a Predictive Model for Calculating Ascent Time to a Target Temperature," Energies, MDPI, vol. 9(12), pages 1-16, December.
    2. Boixo, Sergio & Diaz-Vicente, Marian & Colmenar, Antonio & Castro, Manuel Alonso, 2012. "Potential energy savings from cool roofs in Spain and Andalusia," Energy, Elsevier, vol. 38(1), pages 425-438.
    3. Ciulla, Giuseppina & Lo Brano, Valerio & D’Amico, Antonino, 2016. "Modelling relationship among energy demand, climate and office building features: A cluster analysis at European level," Applied Energy, Elsevier, vol. 183(C), pages 1021-1034.
    4. Ana Ogando & Natalia Cid & Marta Fernández, 2017. "Energy Modelling and Automated Calibrations of Ancient Building Simulations: A Case Study of a School in the Northwest of Spain," Energies, MDPI, vol. 10(6), pages 1-17, June.
    5. Jin Woo Moon & Kyungjae Kim & Hyunsuk Min, 2015. "ANN-Based Prediction and Optimization of Cooling System in Hotel Rooms," Energies, MDPI, vol. 8(10), pages 1-21, September.
    6. Hao Cheng & Xinke Wang & Min Zhou, 2017. "Optimized Design and Feasibility of a Heating System with Energy Storage by Pebble Bed in a Solar Attic," Energies, MDPI, vol. 10(3), pages 1-14, March.
    7. Yu-Ri Kim & Hae Jin Kang, 2016. "Development of a Mobile Application for Building Energy Prediction Using Performance Prediction Model," Energies, MDPI, vol. 9(3), pages 1-16, March.
    8. Chen, Yongbao & Xu, Peng & Chu, Yiyi & Li, Weilin & Wu, Yuntao & Ni, Lizhou & Bao, Yi & Wang, Kun, 2017. "Short-term electrical load forecasting using the Support Vector Regression (SVR) model to calculate the demand response baseline for office buildings," Applied Energy, Elsevier, vol. 195(C), pages 659-670.
    9. Domenico Mazzeo & Giuseppe Oliveti & Natale Arcuri, 2017. "A Method for Thermal Dimensioning and for Energy Behavior Evaluation of a Building Envelope PCM Layer by Using the Characteristic Days," Energies, MDPI, vol. 10(5), pages 1-19, May.
    10. Yildiz, B. & Bilbao, J.I. & Sproul, A.B., 2017. "A review and analysis of regression and machine learning models on commercial building electricity load forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 1104-1122.
    11. Zomorodian, Zahra Sadat & Tahsildoost, Mohammad & Hafezi, Mohammadreza, 2016. "Thermal comfort in educational buildings: A review article," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 895-906.
    12. Jin Woo Moon & Sung Kwon Jung & Yong Oh Lee & Sangsun Choi, 2015. "Prediction Performance of an Artificial Neural Network Model for the Amount of Cooling Energy Consumption in Hotel Rooms," Energies, MDPI, vol. 8(8), pages 1-18, August.
    13. Belusko, M. & Bruno, F. & Saman, W., 2011. "Investigation of the thermal resistance of timber attic spaces with reflective foil and bulk insulation, heat flow up," Applied Energy, Elsevier, vol. 88(1), pages 127-137, January.
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    1. Stéfano Frizzo Stefenon & Roberto Zanetti Freire & Leandro dos Santos Coelho & Luiz Henrique Meyer & Rafael Bartnik Grebogi & William Gouvêa Buratto & Ademir Nied, 2020. "Electrical Insulator Fault Forecasting Based on a Wavelet Neuro-Fuzzy System," Energies, MDPI, vol. 13(2), pages 1-19, January.
    2. Hara Prasada Tripathy & Priyabrata Pattanaik & Dilip Kumar Mishra & William Holderbaum, 2023. "Heat and Moisture Management for Automatic Air Conditioning of a Domestic Household Using FA-ZnO Nanocomposite as Smart Sensing Material," Energies, MDPI, vol. 16(6), pages 1-12, March.

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