Artificial Intelligence for HVAC Diagnostics: Towards the Era of Large Language Models
Chujie Lu (TU Delft - Architecture and the Built Environment)
Christian Struck (Saxion Hogescholen)
Clayton Miller (Singapore Management University)
Dirk Saelens (Katholieke Universiteit Leuven)
Laure Itard (TU Delft - Architecture and the Built Environment)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
Faults silently degrade HVAC performance, wasting energy and diminishing indoor well-being. How can artificial intelligence help us diagnose them? This paper shares insights into challenges of large-scale practical HVAC diagnostics and presents efforts from the Brains4Buildings project, specifically highlighting the emerging potential of Large Language Models (LLMs) as intelligent assistants toward self-learning and adaptive diagnostics.