| Title |
Building heat loss evaluation using artificial intelligence methods and thermal photogrammetry / |
| Authors |
Kardoka, Justas ; Paulauskaite-Taraseviciene, Agne ; Pupeikis, Darius |
| Full Text |
|
| Is Part of |
CEUR workshop proceedings: IVUS 2024: Information society and university studies 2024: proceedings of the 29th international conference on information society and university studies (IVUS 2023) Kaunas, Lithuania, May 17, 2024 / edited by: I. Veitaitė, A. Lopata, T. Krilavičius, M. Woźniak.. Aachen : CEUR-WS. 2024, vol. 3885, p. 1-8.. ISSN 1613-0073 |
| Keywords [eng] |
thermal photogrammetry ; point-clouds ; building heat loss evaluation ; artificial intelligence ; point-cloud segmentation ; building segmentation |
| Abstract [eng] |
Thermal point-clouds are becoming increasingly relevant in times of climate change, and it is essential that efficient methods for calculating heat losses exist. Whilst heat losses can be calculated by means of simulations and / or on-site expertise, such methods can consume significant financial resources. With the rise of artificial intelligence methods and the availability of thermal imaging technologies, they can be utilized for the automation of such calculations. We propose a methodology for calculating heat losses based on thermal photogrammetry and imaging. By segmenting thermal point-clouds for buildings and removing noise from the result of the segmentation, the output is a point-cloud that is void of unnecessary data for heat loss calculations. This model is then converted to a mesh, and heat losses are calculated for each triangle of the mesh by mapping the area of each triangle to the surface temperature of it based on the closest RGB color from the thermal images, resulting in a direct map between triangle surface area and triangle surface temperature. Our results indicate that such a methodology can be used for more efficient heat loss calculations, as we have achieved a mean average error of 0.42 kW or 0.14 kW depending on whether the ground is considered during calculations or not, respectively. Further work could explore calculating heat losses for multiple buildings at a time, calculating heat losses during different seasons. Furthermore, different emissivity and thermal loss coefficients can be used, as using static values for these parameters limits the accuracy of the calculations. |
| Published |
Aachen : CEUR-WS |
| Type |
Conference paper |
| Language |
English |
| Publication date |
2024 |
| CC license |
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