LLMs struggle with HVAC deployment due to data and safety issues

A review of 66 studies on Large Language Models (LLMs) for HVAC operations reveals significant challenges in deploying these agents in building automation systems. The primary hurdles include normalizing heterogeneous sensor data and ensuring operational safety, as LLMs currently lack the reliability for direct control of physical systems. While LLMs show promise in tasks requiring semantic reasoning, such as interpreting unstructured documentation, traditional methods like model predictive control and reinforcement learning remain superior for high-frequency control and numerical forecasting. AI

IMPACT LLMs are not yet ready for direct control in physical systems like HVAC due to safety and latency concerns, but show potential for semantic tasks.

RANK_REASON The item is a systematic review of existing research studies on LLMs in a specific domain (HVAC operations). [lever_c_demoted from research: ic=1 ai=0.7]

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