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

2026-06-22

AI Summary

This work addresses the limitation of current large language models (LLMs) in multi-zone HVAC control, which typically lack explicit modeling of building physics and thermodynamic processes. To bridge this gap, the authors propose a knowledge graph that integrates thermodynamic principles with spatial semantics, constructed upon the Brick ontology and enriched with historical environment-controller interaction data to provide LLMs with structured contextual information. This approach represents the first integration of physics-informed spatial semantic graphs into an LLM-based control framework, explicitly capturing inter-zone thermal couplings and building dynamic responses. Evaluated in a five-zone building simulation, the method significantly improves the trade-off between energy efficiency and occupant comfort compared to both conventional and existing LLM-based strategies, achieving the lowest PMV violation rate while maintaining high energy performance.

Abstract

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.

Problem

• multi-zone HVAC control
• thermal coupling
• building dynamics
• spatial reasoning
• energy-comfort trade-off

Innovation

• ThermoLLM
• spatial-semantic knowledge graph
• thermodynamics-aware control
• multi-zone HVAC
• physics-informed LLM