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README.md

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+---
+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://arxiv.org/abs/XXXX.XXXXX
+  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 [arXiv:XXXX.XXXXX]() (to be released) and visit the main repository [ttm4hvac](https://huggingface.co/spaces/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:
+
+```
+**F. Aran**,  
+*Transfer learning of building dynamics digital twin for HVAC control with Time-series Foundation Model*,  
+arXiv:XXXX.XXXXX, 2025.  
+https://arxiv.org/abs/XXXX.XXXXX
+```

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