One for All: LLM guided zero-shot HVAC control
Abstract
HVAC controllers are widely deployed across buildings with different layouts, sensing configurations, climates, and occupancy patterns. In practice, controllers tuned for one building often degrade when applied to another, leading to inconsistent energy efficiency and occupant comfort. Many learning-based HVAC control methods rely on building-specific training, retraining, or expert intervention, which is often impractical or costly at scale. To address these challenges, we present Gen-HVAC, an LLM- guided, zero-shot HVAC control platform for multi-zone buildings that is trained once and deployed across diverse buildings without retraining. We design a transformer-based HVAC controller that is trained on historical building operation data collected across multiple buildings and generates control actions by conditioning on recent system behavior rather than building-specific models. By using recent temperature measurements, past control actions, and observed system responses, the controller generates HVAC control actions that transfer across buildings and climates with- out retraining, enabling the same model to scale to new buildings. To further improve occupant comfort, we integrate a lightweight language model that allows users to specify comfort preferences directly, without requiring human expertise, manual rule design, or paid external APIs. The system translates these preferences into control objectives that guide the controller without interfering with system dynamics or real-time control. By conditioning on these objectives, the controller switches between operating modes, such as energy-focused or comfort-focused behavior. We evaluate Gen-HVAC across multiple climates and building scenarios using EnergyPlus and validate the system in a real build- ing deployment. Results show consistent improvements over rule- based control, achieving 36.8% energy savings with 28% comfort performance under zero-shot deployment. We also release our plat- form to support reproducibility and enable future research on scal- able, data-driven HVAC control.
System Architecture, Training and Implementation
We devided this entire project into 5 phases. Please go through each step to use our system.
Energy Plus Setup
For this project we use Sinergym and EnergyPlus. Sinergym has also a prebuilt docker version, you can find it here. For installation:
docker pull ghcr.io/ugr-sail/sinergym:2.4.0
After this run the docker container and see next steps
docker run -it \
--name genhvac_container \
-v $(pwd):/workspace \
ghcr.io/ugr-sail/sinergym:2.4.0 \
/bin/bash
Data generation
Trajectory generation is executed via the rollout runner coupled with a behavior policy. Use the data-generation script together with the rollout runner to generate temporally consistent data across different buildings, climates, and envelope/occupancy variants. You can generate datasets using rule-based controllers, learned policies, MPC-style rules, or real-building logs such as Ecobee traces, and the same pipeline will serialize them into a unified trajectory format. The provided rollout utilities support targeted generation for a specific location or building type, as well as generation that mixes envelope variants, weather files, and building archetypes to construct large, diverse training corpora.
All of this needs to be run inside a docker container which has energy plus. If you already have energy plus setup as a native software then you can simply adopt the synergym setup, and generate the sequential training data.
# Inside Docker container
cd /workspace
python trajectory_generator.py \
--manifest patched_reference_data_base/OfficeSmall/reference_database.json \
--output_dir dataset \
--behavior seasonal_reactive \
--time_freq 900 \
Optional multi-building combinations:
python trajectory_generator.py \
--manifest patched_reference_data_base/OfficeMedium/reference_database.json \
--combine_climates True \
--combine_envelopes True \
--output_dir dataset_large
Each episode is stored as compressed .npz:
dataset/
├── OfficeSmall__Buffalo__standard__episode_001.npz
├── OfficeSmall__Dubai__high_internal__episode_002.npz
└── metadata.json
Each file contains:
{
"observations": np.ndarray(T, state_dim),
"actions": np.ndarray(T, action_dim),
"rewards": np.ndarray(T),
"state_keys": list,
"action_keys": list,
"meta": dict
}
Temporal resolution: 15 minutes
Episode length: 35040 timesteps (1 simulation year)
Training Phase
After data generation, you can proceed to the training phase. In our experiments, we generated 2,300+ building–weather–policy combinations, yielding 3M+ sequential state–action transitions. The training pipeline is modular and consists of the dataloader, the Decision Transformer model, the loss modules, and the main training loop.
In most cases, the only required adaptation is mapping your raw sensor observations to the expected schema and defining the corresponding action keys; we provide validated mappings for OfficeSmall STD2013 (5-zone) and OfficeMedium STD2013 (15-zone), and the same interface extends directly to other HOT buildings as well as Ecobee or other real-building datasets. The training implementation is designed for generalization and zero-shot transfer. Scaling to larger and more diverse building types primarily requires increasing model capacity (d_model, layers, heads), the embedding and loss structure can remain unchanged. It supports heterogeneous buildings, zone counts, and sensing modalities. We condition the policy on multi-objective return-to-go (RTG) targets for energy and comfort, and optionally apply Top-K filtering/selection by RTG to bias training toward higher-quality sub-trajectories, enabling the model to learn how different action sequences causally trade off energy consumption and comfort outcomes.
LLM deployment phase
Gen-HVAC supports LLM + Digital Human-in-the-Loop (DHIL) layer that modulates preference/RTG targets and high-level constraints. For local LLM hosting, install Ollama, pull a quantized model ,and launch the service.
On Linux/macOS you can install Ollama via curl -fsSL https://ollama.com/install.sh | sh, start the daemon with ollama serve (leave it running), and pull recommended models using ollama pull deepseek-r1:7b (lightweight reasoning), ollama pull llama3.1:8b (strong general instruction-following), ollama pull qwen2.5:7b (efficient general model), or ollama pull mistral:instruct (fast instruct model). If you want a slightly heavier but still practical model, ollama pull deepseek-r1:14b or ollama pull qwen2.5:14b. In our testing we choose Deepseek R1.
Once pulled, run deepseek-r1:7b with Ollama, then in another terminal point your Gen-HVAC LLM client to the default endpoint and run your integration from the llm/ folder (e.g., python -m llm.server --host 0.0.0.0 --port 8000 and python -m llm.client --base_url http://localhost:xxxx --model deepseek-r1:7b. After the LLM endpoint is up, you can proceed to the inference server step to bind the persona/prompt layer to RTG conditioning and the control loop in one end to end pipeline.
Inference
During inference, we deploy Gen-HVAC as a stateless HTTP microservice that loads the trained Decision Transformer checkpoint and normalization statistics at startup, maintains a short autoregressive context window internally, and returns multi-zone heating/cooling setpoints per control step. In our experiments, EnergyPlus/Sinergym executes inside the Docker container, while the inference service runs on the host/server (CPU/GPU), so the simulator can stream observation vectors to POST /predict (payload: {step, obs, info}) and receive an action vector in the response, with POST /reset used to clear policy history at episode boundaries. When enabled, the DHIL module queries a local Ollama endpoint and updates the comfort RTG target at a low frequency (e.g., every 4 steps).
Repository Structure
Gen-HVAC:Controller/
│
├── data_generator/ # Data generation pipeline (EnergyPlus/Sinergym rollouts)
│ ├── rollout_runner.py # Runs rollouts for selected building-weather configs and logs outputs
│ └── trajectory_generator.py # Creates trajectory datasets from rollouts
│
├── evaluation/ # Evaluation scripts
│
├── Inference_&_LLM/ # Inference + LLM/DHIL (Digital Human-in-the-Loop) components
│ ├── inference.py # Runs local inference (loads model + produces actions)
│ ├── inference_server.py # Server wrapper for inference (API-based deployment)
│ ├── digital_human_in_the_loop.py # DHIL logic
│ └── llm_client/ # LLM client utilities
│ └── ...
│
├── Model/ # Saved model checkpoints + configs
│ ├── Model_V1/
│ │ ├── last.pt # Model checkpoint
│ │ ├── model_config.json # Training/model parameters
│ │ └── report.json
│ ├── Model_V2/ ...
│ ├── Model_V3/ ...
│
├── training/ # Training code (DT model, embeddings, losses, trainer)
│ ├── data_loader.py # Loads trajectories, builds tokens/batches, normalization, RTG, etc.
│ ├── embeddings.py # Feature encoders + token embeddings (zone/global/RTG encodings)
│ ├── losses.py # Action loss + auxiliary losses (physics/value/etc. if enabled)
│ └── training.py # Main training entry point (train loop, checkpoints, logging)
│
└── utilities/ # Shared utilities used across data-gen/training/eval/inference
├── comfort.py # Comfort metric helpers
├── data_generator.py # Shared dataset helpers / schema utilities
├── policy.py # Policy wrappers (DT policy interface, action post-processing)
├── rewards.py
├── rollout.py # Rollout utilities (env stepping, logging, post-processing)
└── tables.py