Building Automation LLMs: What 66 Studies Reveal About Deploying Agents in HVAC Systems

A new systematic review of 66 peer-reviewed studies on using Large Language Models (LLMs) for HVAC operations reveals significant challenges in deploying agents within building automation systems. The review codes every study across five application families and three LLM method families, creating a deployment readiness map that illustrates where LLMs can currently be deployed, where they require human oversight, and where they remain research toys.

One of the primary challenges highlighted is the Metadata Normalization Problem. Building automation systems produce sensor data through various protocols like BACnet, Modbus, and proprietary REST APIs, leading to inconsistent point naming conventions. An LLM agent processing HVAC state must first map these heterogeneous names to a canonical schema, which the review identifies as a near-term use case.

Several studies use Retrieval-Augmented Generation (RAG) to ground LLM outputs in building-specific documentation, creating a mapping table from raw point names to standardized ontologies like Brick or Haystack.

The review classifies studies into five application families: Building Energy Modeling (BEM), Fault Detection & Diagnostics, Control & Optimization, Load Forecasting, and Occupant Interaction. Each family is assigned a deployment readiness score: near-term, research-only, or reserved for future research. BEM and Fault Detection & Diagnostics are considered near-term, while Control & Optimization, Load Forecasting, and Occupant Interaction are deemed research-only due to the complexity and safety concerns involved.

Twelve studies focus on Control & Optimization, but their findings consistently show that LLMs generate control policies or setpoint schedules that are executed by physics-based controllers rather than the LLM itself. This architecture ensures safety and feasibility, as LLMs lack the ability to directly control actuators and manage latency issues inherent in HVAC control loops.

Conventional ML, Model Predictive Control (MPC), and Reinforcement Learning (RL) are more widely adopted for high-frequency control due to their bounded failure modes and reliability.

Sensor Data Heterogeneity and Failure Modes present another significant hurdle. Sensor readings in building automation systems can be stale, missing, or contradictory, requiring agents to handle issues such as stale data, missing metadata, and contradictory readings. However, none of the 66 studies implement production-grade error handling strategies, as most assume clean, labeled, and synchronized sensor streams, which is not realistic in real-world scenarios.

The most deployable pattern identified in the review is document-grounded operator support, where LLM agents retrieve relevant documentation (control sequences, equipment manuals, maintenance logs) and respond to operator queries in natural language. A typical RAG pipeline for HVAC operator support involves indexing building documentation, embedding the documents, and using a Retrieval-QA chain to answer queries.

Despite its potential, only a limited number of studies have successfully deployed such systems, leaving a significant gap between research prototypes and production systems.