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Scientific paper - Original scientific paper
Combining Deep Learning and the Heat Flux Method for In-Situ Thermal-Transmittance Measurement Improvement
Energies (Basel), 15(14) (2022); 5029. urn:nbn:hr:237:268585
Gumbarević, Sanjin; Milovanović, Bojan; Dalbelo Bašić, Bojana; Gaši, Mergim

Institutional repository: Repository of the Faculty of Civil Engineering, University of Zagreb

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Gumbarević, S., Milovanović, B., Dalbelo Bašić, B. & Gaši, M. (2022). Combining Deep Learning and the Heat Flux Method for In-Situ Thermal-Transmittance Measurement Improvement. Energies (Basel), 15(14).. Retrieved from https://urn.nsk.hr/urn:nbn:hr:237:268585

Gumbarević, Sanjin, et al. "Combining Deep Learning and the Heat Flux Method for In-Situ Thermal-Transmittance Measurement Improvement." Energies (Basel), vol. 15(14), 2022. https://urn.nsk.hr/urn:nbn:hr:237:268585

Gumbarević, Sanjin, Bojan Milovanović, Bojana Dalbelo Bašić and Mergim Gaši. "Combining Deep Learning and the Heat Flux Method for In-Situ Thermal-Transmittance Measurement Improvement." Energies (Basel) 15(14) (2022). https://urn.nsk.hr/urn:nbn:hr:237:268585

Gumbarević, S., et al. (2022) 'Combining Deep Learning and the Heat Flux Method for In-Situ Thermal-Transmittance Measurement Improvement', Energies (Basel), 15(14). Available at: https://urn.nsk.hr/urn:nbn:hr:237:268585 (Accessed 05 October 2024)

Gumbarević S, Milovanović B, Dalbelo Bašić B, Gaši M. Combining Deep Learning and the Heat Flux Method for In-Situ Thermal-Transmittance Measurement Improvement. Energies (Basel) [Internet]. 2022 [cited 2024 October 05];15(14). Available at: https://urn.nsk.hr/urn:nbn:hr:237:268585

S. Gumbarević, B. Milovanović, B. Dalbelo Bašić and M. Gaši, "Combining Deep Learning and the Heat Flux Method for In-Situ Thermal-Transmittance Measurement Improvement", Energies (Basel), vol. 15(14), 2022. [Online]. Available at: https://urn.nsk.hr/urn:nbn:hr:237:268585. [Accessed: 05 October 2024]

Metadata
Title (english)Combining Deep Learning and the Heat Flux Method for In-Situ Thermal-Transmittance Measurement Improvement
AuthorSanjin Gumbarević
AuthorBojan Milovanović
AuthorBojana Dalbelo Bašić
AuthorMergim Gaši
Author's institutionUniversity of Zagreb
Faculty of Civil Engineering
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Civil Engineering
Abstract (english)
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.
Keywords (english)
Languageenglish
Publication typeScientific paper - Original scientific paper
Publication statusPublished
Peer reviewPeer review - international
Publication versionPublished version
Journal titleEnergies (Basel)
Numberingvol. 15(14), 5029
e-ISSN1996-1073
URN:NBNurn:nbn:hr:237:268585
Publication2022
Type of resourceText
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2024-09-05 04:56:19