# ThermoLLM: Thermodynamics-Aware HVAC Control with Spatial-Semantic Knowledge Graph

arXiv: https://arxiv.org/abs/2606.22911 · HTML: https://mlsanity.com/paper/2606.22911/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 22 references that are in the map.
- First posted on arXiv 22 Jun 2026.

## Builds on (most cited first, 7 of 7)

- [LLMSense: Harnessing LLMs for High-level Reasoning Over Spatiotemporal Sensor Traces](https://mlsanity.com/paper/2403.19857/) (2024)
- [OG-RAG: Ontology-Grounded Retrieval-Augmented Generation For Large Language Models](https://mlsanity.com/paper/2412.15235/) (2024)
- [IoT-LLM: a framework for enhancing Large Language Model reasoning from real-world sensor data](https://mlsanity.com/paper/2410.02429/) (2024)
- [Multi-zone HVAC Control with Model-Based Deep Reinforcement Learning](https://mlsanity.com/paper/2302.00725/) (2023)
- [A Comparison of Classical and Deep Reinforcement Learning Methods for HVAC Control](https://mlsanity.com/paper/2308.05711/) (2023)
- [An experimental evaluation of Deep Reinforcement Learning algorithms for HVAC control](https://mlsanity.com/paper/2401.05737/) (2024)
- [A Scalable and Interoperable Platform for Transforming Building Information with Brick Ontology](https://mlsanity.com/paper/2509.16259/) (2025)

## Built on by (most cited first, 2 of 2)

- [Exact Action Values Are Not Enough: Rollout-Verified Reinforcement Fine-Tuning of a Reasoning Model for Multi-Zone VAV Control](https://mlsanity.com/paper/2607.27914/) (2026)
- [Narrow and Deep: An Ontology Tower as the Knowledge of an LLM Agent for an Industrial Equipment System](https://mlsanity.com/paper/2610.11768/) (2026)

## Abstract (arXiv)

Multi-zone HVAC control is a spatial decision problem in which indoor thermal evolution and control decisions depend not only on outdoor conditions and internal heat gains but also on zone layout, physical adjacency, and delayed thermal interactions across the building. Recent LLM-based HVAC controllers have shown that prompt-based control is feasible. However, these methods typically rely on task descriptions, observation values, short textual feedback, or unstructured retrieval, which limits their ability to reason about zone coupling, thermal response, and building dynamics. This paper presents a thermodynamics-aware LLM control framework for a five-zone EnergyPlus building simulation. The controller is grounded in a physics-informed spatial knowledge graph derived from Brick-style building semantics and linked with recent interaction history. At each control step, the model receives the current building state, graph-structured spatial context, and recent environment-controller history, enabling it to make decisions that reflect both building structure and short-term thermal evolution. We evaluate the framework against standard control baselines and several LLM-based alternatives. Results show that the proposed approach achieves the best overall energy-comfort trade-off and the lowest PMV violation while maintaining energy-efficient operation.