用物理约束的AI自动生成可解释的空调故障检测规则
Physics-Informed Large Language Models for HVAC Anomaly Detection with Autonomous Rule Generation
- 构建进化循环框架，自动生成并优化故障规则
- 在公开数据集上达到顶尖性能，规则可解释且可执行
- 适合智能建筑运维与可信AI落地场景
暖通空调（HVAC）系统占全球建筑能耗的很大比例，可靠的异常检测对提升能效、降低排放至关重要。传统规则方法虽可解释但难以适应，深度学习虽具预测力却缺乏透明性、效率及物理合理性。现有基于大语言模型（LLM）的方法改善了可解释性，但普遍忽略空调运行的物理规律。本文提出PILLM——一种物理感知的LLM框架，通过进化循环自动生成、评估与优化异常检测规则。该方法引入物理感知的反思与交叉算子，嵌入热力学与控制理论约束，使规则兼具自适应性与物理合理性。在公开的建筑故障检测数据集上的实验表明，PILLM实现领先性能，同时生成可解释、可操作的诊断规则，推动智能建筑系统中可信且可部署的AI发展。
原文摘要 · Abstract (English)
Heating, Ventilation, and Air-Conditioning (HVAC) systems account for a substantial share of global building energy use, making reliable anomaly detection essential for improving efficiency and reducing emissions. Classical rule-based approaches offer explainability but lack adaptability, while deep learning methods provide predictive power at the cost of transparency, efficiency, and physical plausibility. Recent attempts to use Large Language Models (LLMs) for anomaly detection improve interpretability but largely ignore the physical principles that govern HVAC operations. We present PILLM, a Physics-Informed LLM framework that operates within an evolutionary loop to automatically generate, evaluate, and refine anomaly detection rules. Our approach introduces physics-informed reflection and crossover operators that embed thermodynamic and control-theoretic constraints, enabling rules that are both adaptive and physically grounded. Experiments on the public Building Fault Detection dataset show that PILLM achieves state-of-the-art performance while producing diagnostic rules that are interpretable and actionable, advancing trustworthy and deployable AI for smart building systems.
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