Large Language Models for HVAC Operations in Building Energy Systems: A Critical Review of Methods, Applications, and Deployment Readiness
Systematic review of 66 studies finds LLMs for HVAC operations are mostly research-stage, with no ready-now deployment and only four pilot-level studies.
A critical review of 66 peer-reviewed studies from 2023 to March 2026 examines LLMs for HVAC operations in building energy systems. Only four studies reach pilot-level evidence, none reports sustained operational deployment, and 63 of 66 are research-only. Conventional ML, MPC, and RL remain dominant for high-frequency control and short-horizon forecasting, and the evidence supports LLMs primarily as semantic and workflow layers rather than autonomous controllers.
- Only 4 of 66 studies reach pilot-level evidence, none ready-now for industry
- Building energy modelling dominates the corpus (32 of 66 papers)
- Bounded human-in-the-loop uses merit near-term trials, not autonomous control
- Future work should prioritize field-validated benchmarks and LLM-MPC/RL architectures
From the original article
1 min read at arxiv.orgBuilding 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.
By Alexander Neubauer · arxiv.org
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