---
pretty_name: TTM4HVAC – Training dataset (source-all) 
tags:
- ttm4hvac
- hvac
- time-series
- energy
task_categories:
- time-series-forecasting
papers:
- title: "Transfer learning of building dynamics digital twin for HVAC control with Time-series Foundation Model"
  url: https://doi.org/10.1016/j.mex.2026.103866
  authors: "Ferran Aran Domingo"
license: mit
---

# TTM4HVAC – Training dataset (source-all) 

This dataset contains HVAC and weather time-series data used to train the **source-all** TinyTimeMixer model (`gft/ttm4hvac`), the main model of the TTM4HVAC project.

It aggregates all available source-building data under *default* and *non-default* conditions.

Check out the paper [A reproducible method to generate multi-building, multi-climate HVAC operation datasets with a stochastic exploratory controller](https://doi.org/10.1016/j.mex.2026.103866) and visit the main repository [ttm4hvac](https://huggingface.co/gft/ttm4hvac) for further details.

## Columns

- `time`
- `Outdoor Air Temperature (C)`
- `Heating Setpoint (C)`
- `Cooling Setpoint (C)`
- `Room Air Temperature (C)`
- `Outdoor Humidity (%)`
- `Wind Speed (m/s)`
- `Direct Solar Radiation (W/m^2)`
- `HVAC Power Consumption (W)`
- `series_id`
- `is_default`

## Usage

```python
from datasets import load_dataset

ds = load_dataset("gft/ttm4hvac-source-all-train")
df = ds["train"].to_pandas()
```

# ✒️ Citation

If you use this model or datasets, please cite:

```bibtex
@article{aran_domingo_2026_hvac_dataset,
  title   = {A reproducible method to generate multi-building, multi-climate HVAC operation datasets with a stochastic exploratory controller},
  author  = {Aran Domingo, Ferran and
             Fraile Alonso, Pablo and
             Rius Torrentó, Josep and
             Agost Batalla, Oriol and
             Barri Vilardell, Ignasi and
             Vilaplana Mayoral, Jordi and
             Mateo Fornés, Jordi},
  journal = {MethodsX},
  volume  = {16},
  pages   = {103866},
  year    = {2026},
  doi     = {10.1016/j.mex.2026.103866},
  url     = {https://doi.org/10.1016/j.mex.2026.103866}
}
```