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Combining Deep Learning and the Heat Flux Method for In-Situ Thermal-Transmittance Measurement Improvement

Author

Listed:
  • Sanjin Gumbarević

    (Ericsson Nikola Tesla, Krapinska 45, 10000 Zagreb, Croatia)

  • Bojan Milovanović

    (Faculty of Civil Engineering, University of Zagreb, 10000 Zagreb, Croatia)

  • Bojana Dalbelo Bašić

    (Faculty of Electrical Engineering and Computing, University of Zagreb, 10000 Zagreb, Croatia)

  • Mergim Gaši

    (Faculty of Civil Engineering, University of Zagreb, 10000 Zagreb, Croatia)

Abstract

Transmission losses through the building envelope account for a large proportion of building energy balance. One of the most important parameters for determining transmission losses is thermal transmittance. Although thermal transmittance does not take into account dynamic parameters, it is traditionally the most commonly used estimation of transmission losses due to its simplicity and efficiency. It is challenging to estimate the thermal transmittance of an existing building element because thermal properties are commonly unknown or not all the layers that make up the element can be found due to technical-drawing information loss. In such cases, experimental methods are essential, the most common of which is the heat-flux method (HFM). One of the main drawbacks of the HFM is the long measurement duration. This research presents the application of deep learning on HFM results by applying long-short term memory units on temperature difference and measured heat flux. This deep-learning regression problem predicts heat flux after the applied model is properly trained on temperature-difference input, which is backpropagated by measured heat flux. The paper shows the performance of the developed procedure on real-size walls under the simulated environmental conditions, while the possibility of practical application is shown in pilot in-situ measurements.

Suggested Citation

  • Sanjin Gumbarević & Bojan Milovanović & Bojana Dalbelo Bašić & Mergim Gaši, 2022. "Combining Deep Learning and the Heat Flux Method for In-Situ Thermal-Transmittance Measurement Improvement," Energies, MDPI, vol. 15(14), pages 1-19, July.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:14:p:5029-:d:859384
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    References listed on IDEAS

    as
    1. David Bienvenido-Huertas, 2020. "Assessing the Environmental Impact of Thermal Transmittance Tests Performed in Façades of Existing Buildings: The Case of Spain," Sustainability, MDPI, vol. 12(15), pages 1-18, August.
    2. Alessia Buda & Ernst Jan de Place Hansen & Alexander Rieser & Emanuela Giancola & Valeria Natalina Pracchi & Sara Mauri & Valentina Marincioni & Virginia Gori & Kalliopi Fouseki & Cristina S. Polo Lóp, 2021. "Conservation-Compatible Retrofit Solutions in Historic Buildings: An Integrated Approach," Sustainability, MDPI, vol. 13(5), pages 1-19, March.
    3. Bienvenido-Huertas, David & Moyano, Juan & Marín, David & Fresco-Contreras, Rafael, 2019. "Review of in situ methods for assessing the thermal transmittance of walls," Renewable and Sustainable Energy Reviews, Elsevier, vol. 102(C), pages 356-371.
    4. Hassan Bazazzadeh & Peiman Pilechiha & Adam Nadolny & Mohammadjavad Mahdavinejad & Seyedeh sara Hashemi safaei, 2021. "The Impact Assessment of Climate Change on Building Energy Consumption in Poland," Energies, MDPI, vol. 14(14), pages 1-17, July.
    5. Sanjin Gumbarević & Ivana Burcar Dunović & Bojan Milovanović & Mergim Gaši, 2020. "Method for Building Information Modeling Supported Project Control of Nearly Zero-Energy Building Delivery," Energies, MDPI, vol. 13(20), pages 1-21, October.
    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. Luca Evangelisti & Andrea Scorza & Roberto De Lieto Vollaro & Salvatore Andrea Sciuto, 2022. "Comparison between Heat Flow Meter (HFM) and Thermometric (THM) Method for Building Wall Thermal Characterization: Latest Advances and Critical Review," Sustainability, MDPI, vol. 14(2), pages 1-18, January.
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