发表机构
Technische Universität Berlin; Lawrence Berkeley National Laboratory（柏林工业大学; 劳伦斯伯克利国家实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该综述分析2023年至2026年3月的66篇LLM用于HVAC运行的研究，发现LLM目前主要作为语义和工作流层，仅少数研究达试点级，无持续部署，建议开展LLM与MPC/RL结合的研究。
AI 中文摘要
建筑自动化系统会生成丰富的传感器数据，但由于异构的点命名、缺失的元数据以及碎片化的文档阻碍了其在运行中的使用，因此仍缺乏洞察力。本系统综述分析并编码了2023年至2026年3月期间发表的66篇关于大语言模型（LLM）用于HVAC运行的同行评审研究。每篇研究被分为5个应用类别和3个LLM方法类别，并评估了证据真实性、部署就绪情况以及LLM与物理HVAC决策之间的责任边界。该语料集中在建筑能源建模（BEM，66篇论文中的32篇），而负荷预测领域的研究过少，无法得出子领域层面的结论。仅有4项研究达到了试点级别的证据，且没有一项报告了持续的运行部署。没有研究被归类为可立即供行业采用；3项属于近期可采用，63项仅为研究性质。不过，一些有限的、有人参与的用途值得近期试验，包括点名称标准化、基于文档的操作员支持、BEM工作流辅助以及基于物理控制器的咨询接口。传统机器学习（ML）、模型预测控制（MPC）、强化学习（RL）和基于本体的工具在高频控制、短时间范围数值预测以及定义明确的本体映射方面仍被更多采用，而自主智能体操作和未经验证的居住者代理仍处于研究阶段。因此，当前证据支持LLM主要作为语义和工作流层，而非自主HVAC控制器。未来工作应优先考虑现场验证的基准、运行约束下的编排评估，以及具有有限延迟和可验证安全属性的LLM-MPC/RL架构。
英文摘要
Building automation systems generate rich sensor data yet remain insight-poor because heterogeneous point naming, missing metadata, and fragmented documentation obstruct their operational use. This systematic review analyses and codes 66 peer-reviewed studies on large language models (LLMs) for HVAC operations published between 2023 and March 2026. Each study is classified across five application families and three LLM method families and assessed for evidence realism, deployment readiness, and the responsibility boundary between the LLM and physical HVAC decisions. The corpus is concentrated in building energy modelling (BEM, 32 of 66 papers), while load forecasting remains too sparse for subfield-level conclusions. Only four studies reach pilot-level evidence, and none reports sustained operational deployment. No study was classified as ready-now for industry adoption; three were near-term and 63 research-only. Nevertheless, several bounded, human-in-the-loop uses merit near-term trials, including point-name normalisation, document-grounded operator support, BEM workflow assistance, and advisory interfaces around physics-based controllers. Conventional machine learning (ML), model predictive control (MPC), reinforcement learning (RL) and ontology-based tools remain more adopted for high-frequency control, short-horizon numerical forecasting, and well-posed ontology mapping, while autonomous agentic operation and unvalidated occupant proxies remain research-stage. Current evidence therefore supports LLMs primarily as semantic and workflow layers rather than autonomous HVAC controllers. Future work should prioritise field-validated benchmarks, orchestration evaluation under operational constraints, and LLM-MPC/RL architectures with bounded latency and verifiable safety properties.
Comments38 pages, 9 figures, 16 tables. Submitted to Energy and Buildings
