arXiv AI By 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

Read the original on arXiv AI →

The paper reviews 66 studies on large language models (LLMs) applied to HVAC operations in building energy systems, categorizing them by application and method families and evaluating their evidence realism and deployment readiness. It finds that most work focuses on building energy modelling, with only four studies reaching pilot-level evidence and none reporting sustained operational deployment. LLMs are currently best suited as semantic and workflow layers—such as point‑name normalisation and document‑grounded operator support—rather than autonomous HVAC controllers, and future research should target field‑validated benchmarks and safe, low‑latency LLM‑MPC/RL integrations.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Machine Learning
Jul 20

Comparative Field Deployment of Reinforcement Learning and Model Predictive Control for Residential HVAC

arXiv:2510. 01475v2 Announce Type: replace-cross Abstract: Model Predictive Control (MPC) has demonstrated significant performance improvements over today's control methods for residential Heating, Ventilation, and Air Conditioning (HVAC), but deploying MPC often requires substantial engineering effort.

By Ozan Baris Mulayim, Elias N. Pergantis, Levi D. Reyes Premer, Bingqing Chen, Guannan Qu, Kevin J. Kircher, Mario Berg\'es
arXiv Machine Learning
Jul 8

Life Cycle Assessment of Pre-training the Lucie 7B Open-Source Large Language Model on the Jean Zay Supercomputer

arXiv:2607. 05408v1 Announce Type: cross Abstract: The environmental impact of training large language models (LLMs) is increasingly scrutinised, yet most published estimates focus on operational energy and disclose little about manufacturing (embodied) emissions, water consumption, or the underlying highperformance computing (HPC) infrastructure.

By Marc L\'eobet, Pierre-Fran\c{c}ois Lavall\'ee, Jean-Pierre Lorr\'e