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LLMs show promise for HVAC but aren't ready for industry adoption

By PulseAugur Editorial · [1 sources] ·

A recent review of 66 studies published between 2023 and March 2026 indicates that while large language models (LLMs) show promise for HVAC operations in building energy systems, they are not yet ready for widespread industry adoption. The research primarily focuses on building energy modeling, with limited progress in load forecasting. Currently, LLMs are best suited for semantic and workflow tasks, such as point-name normalization and operator support, rather than autonomous control, which still relies on conventional machine learning and model predictive control methods. AI

IMPACT LLMs are currently best suited for semantic and workflow tasks in building energy systems, rather than autonomous control.

RANK_REASON The cluster contains a peer-reviewed academic paper detailing a systematic review of existing research. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLMs show promise for HVAC but aren't ready for industry adoption

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The cluster contains a peer-reviewed academic paper detailing a systematic review of existing research. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Alexander Neubauer, Tianzhen Hong, Han Li, Mengbo Yu, Amin Darbandi, Yannick F\"urst, Martin Kriegel ·

    Large Language Models for HVAC Operations in Building Energy Systems: A Critical Review of Methods, Applications, and Deployment Readiness

    arXiv:2609.05314v1 Announce Type: new Abstract: 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 cod…

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