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Application of artificial neural network to predict thermal transmittance of wooden windows

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  • Buratti, Cinzia
  • Barelli, Linda
  • Moretti, Elisa

Abstract

Thermal performance of windows depends on many parameters, such as dimensional characteristics and material properties of the components. The thermal transmittance U can be evaluated by a numerical method based on the CFD approach for the evaluation of the frame U-value (ISO 10077-1, ISO 10077-2) or by experimental campaigns on window prototypes, according to ISO 12657-1; in both cases significant effort and time are required.

Suggested Citation

  • Buratti, Cinzia & Barelli, Linda & Moretti, Elisa, 2012. "Application of artificial neural network to predict thermal transmittance of wooden windows," Applied Energy, Elsevier, vol. 98(C), pages 425-432.
    • Handle: RePEc:eee:appene:v:98:y:2012:i:c:p:425-432
    DOI: 10.1016/j.apenergy.2012.04.004
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    References listed on IDEAS

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    Cited by:

    1. Fang, Guochang & Tian, Lixin & Fu, Min & Sun, Mei, 2013. "The impacts of carbon tax on energy intensity and economic growth – A dynamic evolution analysis on the case of China," Applied Energy, Elsevier, vol. 110(C), pages 17-28.
    2. Francesco Asdrubali & Cinzia Buratti & Franco Cotana & Giorgio Baldinelli & Michele Goretti & Elisa Moretti & Catia Baldassarri & Elisa Belloni & Francesco Bianchi & Antonella Rotili & Marco Vergoni &, 2013. "Evaluation of Green Buildings’ Overall Performance through in Situ Monitoring and Simulations," Energies, MDPI, vol. 6(12), pages 1-23, December.
    3. Hwang, Jun Kwon & Yun, Geun Young & Lee, Sukho & Seo, Hyeongjoon & Santamouris, Mat, 2020. "Using deep learning approaches with variable selection process to predict the energy performance of a heating and cooling system," Renewable Energy, Elsevier, vol. 149(C), pages 1227-1245.
    4. von Grabe, Jörn, 2016. "Potential of artificial neural networks to predict thermal sensation votes," Applied Energy, Elsevier, vol. 161(C), pages 412-424.
    5. Malvoni, Maria & Baglivo, Cristina & Congedo, Paolo Maria & Laforgia, Domenico, 2016. "CFD modeling to evaluate the thermal performances of window frames in accordance with the ISO 10077," Energy, Elsevier, vol. 111(C), pages 430-438.
    6. Bienvenido-Huertas, David & Moyano, Juan & Rodríguez-Jiménez, Carlos E. & Marín, David, 2019. "Applying an artificial neural network to assess thermal transmittance in walls by means of the thermometric method," Applied Energy, Elsevier, vol. 233, pages 1-14.
    7. Elisa Moretti & Emanuele Bonamente & Cinzia Buratti & Franco Cotana, 2013. "Development of Innovative Heating and Cooling Systems Using Renewable Energy Sources for Non-Residential Buildings," Energies, MDPI, vol. 6(10), pages 1-16, October.
    8. Buratti, C. & Barbanera, M. & Palladino, D., 2014. "An original tool for checking energy performance and certification of buildings by means of Artificial Neural Networks," Applied Energy, Elsevier, vol. 120(C), pages 125-132.
    9. Iole Nardi & Elena Lucchi, 2023. "In Situ Thermal Transmittance Assessment of the Building Envelope: Practical Advice and Outlooks for Standard and Innovative Procedures," Energies, MDPI, vol. 16(8), pages 1-31, April.
    10. Ghosh, Soumya & Chakraborty, Tilottama & Saha, Satyabrata & Majumder, Mrinmoy & Pal, Manish, 2016. "Development of the location suitability index for wave energy production by ANN and MCDM techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 1017-1028.

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