Datasets:
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pretty_name: TTM4HVAC – Training dataset (source-default)
tags:
- ttm4hvac
- hvac
- time-series
- energy
task_categories:
- time-series-forecasting
license: mit
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"
---
# TTM4HVAC – Training dataset (source-default)
This dataset contains HVAC and weather time-series data collected under **default building control schedules** for the source domain.
It is used to train the `gft/ttm4hvac-source-default` model.
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-default-train")
df = ds["train"].to_pandas()
df.head()
```
# ✒️ 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}
}
``` |