Retrieval‑Augmented LLM Framework for Robust Automated Generation of HVAC Diagnostic Models
Abstract
Diagnostic Bayesian networks (DBNs) offer effective and interpretable solutions for HVAC fault detection and diagnosis (FDD), yet their practical construction and deployment require substantial manual effort. The work proposes a retrieval-augmented LLM framework to achieve robust and automated DBN generation. Preliminary results demonstrate that the proposed framework effectively mitigates hallucinations and significantly improves the structural quality of the generated DBNs.
Fields of Study & Subjects
Building Energy and Comfort Optimization
Advanced Manufacturing and Logistics Optimization
Anomaly Detection Techniques and Applications
Publication details
- Journal / source
- Proceedings of the 2026 ACM Sustainability Week
- Publisher
- ACM
- Pages
- 588–590
- Publication date
- 20 June 2026