Bentley, PJ;
Lim, SL;
Mathur, R;
Narang, S;
(2024)
Automated Real-World Sustainability Data Generation from Images of Buildings.
In:
International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2024.
(pp. 01-06).
IEEE: Male, Maldives.
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Abstract
When data on features of buildings is unavailable, the task of determining how to improve those buildings in terms of carbon emissions becomes infeasible. We show that from only a set of images, a Large Language Model with appropriate prompt engineering and domain knowledge can successfully estimate a range of building features relevant for sustainability calculations. We compare our novel image-To-data method with a ground truth comprising real building data for 47 apartments and achieve accuracy better than a human performing the same task. We also demonstrate that the method can generate tailored recommendations to the owner on how best to improve their properties and discuss methods to scale the approach.
| Type: | Proceedings paper |
|---|---|
| Title: | Automated Real-World Sustainability Data Generation from Images of Buildings |
| Event: | 2024 4th International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME) |
| Dates: | 4 Nov 2024 - 6 Nov 2024 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/ICECCME62383.2024.10797127 |
| Publisher version: | https://doi.org/10.1109/iceccme62383.2024.10797127 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Knowledge engineering , Energy consumption , Accuracy , Mechatronics , Large language models , Buildings , Estimation , Data collection , Prompt engineering , Sustainable development |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10204384 |
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