IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v10y2017i8p1093-d105884.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/10/8/1093/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/10/8/1093/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Qiangqiang Cheng & Yiqi Yan & Shichao Liu & Chunsheng Yang & Hicham Chaoui & Mohamad Alzayed, 2020. "Particle Filter-Based Electricity Load Prediction for Grid-Connected Microgrid Day-Ahead Scheduling," Energies, MDPI, vol. 13(24), pages 1-15, December.
    2. Lee, Juyong & Cho, Youngsang, 2022. "National-scale electricity peak load forecasting: Traditional, machine learning, or hybrid model?," Energy, Elsevier, vol. 239(PD).
    3. Germán Ramos Ruiz & Carlos Fernández Bandera, 2017. "Validation of Calibrated Energy Models: Common Errors," Energies, MDPI, vol. 10(10), pages 1-19, October.
    4. Kei Hirose & Keigo Wada & Maiya Hori & Rin-ichiro Taniguchi, 2020. "Event Effects Estimation on Electricity Demand Forecasting," Energies, MDPI, vol. 13(21), pages 1-20, November.
    5. Zhang, Wenyu & Chen, Qian & Yan, Jianyong & Zhang, Shuai & Xu, Jiyuan, 2021. "A novel asynchronous deep reinforcement learning model with adaptive early forecasting method and reward incentive mechanism for short-term load forecasting," Energy, Elsevier, vol. 236(C).
    6. Barman, Mayur & Dev Choudhury, Nalin Behari, 2019. "Season specific approach for short-term load forecasting based on hybrid FA-SVM and similarity concept," Energy, Elsevier, vol. 174(C), pages 886-896.
    7. Ding, Jia & Wang, Maolin & Ping, Zuowei & Fu, Dongfei & Vassiliadis, Vassilios S., 2020. "An integrated method based on relevance vector machine for short-term load forecasting," European Journal of Operational Research, Elsevier, vol. 287(2), pages 497-510.
    8. Cai, Mengmeng & Pipattanasomporn, Manisa & Rahman, Saifur, 2019. "Day-ahead building-level load forecasts using deep learning vs. traditional time-series techniques," Applied Energy, Elsevier, vol. 236(C), pages 1078-1088.
    9. Guo, Zhifeng & Zhou, Kaile & Zhang, Xiaoling & Yang, Shanlin, 2018. "A deep learning model for short-term power load and probability density forecasting," Energy, Elsevier, vol. 160(C), pages 1186-1200.
    10. Chen, Yongbao & Chen, Zhe & Xu, Peng & Li, Weilin & Sha, Huajing & Yang, Zhiwei & Li, Guowen & Hu, Chonghe, 2019. "Quantification of electricity flexibility in demand response: Office building case study," Energy, Elsevier, vol. 188(C).
    11. Villa-Arrieta, Manuel & Sumper, Andreas, 2018. "A model for an economic evaluation of energy systems using TRNSYS," Applied Energy, Elsevier, vol. 215(C), pages 765-777.
    12. Carolina Rodriguez & María Coronado & Marta D’Alessandro & Juan Medina, 2019. "The Importance of Standardised Data-Collection Methods in the Improvement of Thermal Comfort Assessment Models for Developing Countries in the Tropics," Sustainability, MDPI, vol. 11(15), pages 1-22, August.
    13. Guelpa, Elisa & Bischi, Aldo & Verda, Vittorio & Chertkov, Michael & Lund, Henrik, 2019. "Towards future infrastructures for sustainable multi-energy systems: A review," Energy, Elsevier, vol. 184(C), pages 2-21.
    14. Qin, Yinghong, 2015. "A review on the development of cool pavements to mitigate urban heat island effect," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 445-459.
    15. Dong Eun Jung & Chanuk Lee & Kwang Ho Lee & Minjae Shin & Sung Lok Do, 2021. "Evaluation of Building Energy Performance with Optimal Control of Movable Shading Device Integrated with PV System," Energies, MDPI, vol. 14(7), pages 1-21, March.
    16. Bingjie Jin & Guihua Zeng & Zhilin Lu & Hongqiao Peng & Shuxin Luo & Xinhe Yang & Haojun Zhu & Mingbo Liu, 2022. "Hybrid LSTM–BPNN-to-BPNN Model Considering Multi-Source Information for Forecasting Medium- and Long-Term Electricity Peak Load," Energies, MDPI, vol. 15(20), pages 1-20, October.
    17. Jihoon Moon & Junhong Kim & Pilsung Kang & Eenjun Hwang, 2020. "Solving the Cold-Start Problem in Short-Term Load Forecasting Using Tree-Based Methods," Energies, MDPI, vol. 13(4), pages 1-37, February.
    18. Tsoumalis, Georgios I. & Bampos, Zafeirios N. & Biskas, Pandelis N. & Keranidis, Stratos D. & Symeonidis, Polychronis A. & Voulgarakis, Dimitrios K., 2022. "A novel system for providing explicit demand response from domestic natural gas boilers," Applied Energy, Elsevier, vol. 317(C).
    19. Lu, Yakai & Tian, Zhe & Zhou, Ruoyu & Liu, Wenjing, 2021. "A general transfer learning-based framework for thermal load prediction in regional energy system," Energy, Elsevier, vol. 217(C).
    20. Giovanni Pau & Mario Collotta & Antonio Ruano & Jiahu Qin, 2017. "Smart Home Energy Management," Energies, MDPI, vol. 10(3), pages 1-5, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:10:y:2017:i:8:p:1093-:d:105884. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.