gft/ttm4hvac-target-default
Time Series Forecasting • 943k • Updated
• 2
Datasets:
time string | Room Air Temperature (C) float64 | Outdoor Air Temperature (C) float64 | Outdoor Humidity (%) float64 | Direct Solar Radiation (W/m^2) float64 | Wind Speed (m/s) float64 | Cooling Setpoint (C) float64 | Heating Setpoint (C) float64 | HVAC Power Consumption (W) float64 | series_id int64 |
|---|---|---|---|---|---|---|---|---|---|
2019-02-01 00:00:00 | 16.096824 | -7.888248 | 0.75 | 0 | 2.708807 | 30 | 15 | 0 | 0 |
2019-02-01 00:15:00 | 15.97387 | -7.42054 | 0.75 | 0 | 2.754325 | 30 | 15 | 0 | 0 |
2019-02-01 00:30:00 | 15.856923 | -6.942734 | 0.75 | 0 | 2.820624 | 30 | 15 | 0 | 0 |
2019-02-01 00:45:00 | 15.74324 | -6.502096 | 0.75 | 0 | 2.87958 | 30 | 15 | 0 | 0 |
2019-02-01 01:00:00 | 15.629223 | -6.148435 | 0.75 | 0 | 2.912083 | 30 | 15 | 0 | 0 |
2019-02-01 01:15:00 | 15.510903 | -5.910945 | 0.75 | 0 | 2.937083 | 30 | 15 | 0 | 0 |
2019-02-01 01:30:00 | 15.38714 | -5.758935 | 0.75 | 0 | 2.962083 | 30 | 15 | 0 | 0 |
2019-02-01 01:45:00 | 15.25954 | -5.651153 | 0.75 | 0 | 2.987083 | 30 | 15 | 0 | 0 |
2019-02-01 02:00:00 | 15.130458 | -5.551004 | 0.75 | 0 | 3.012083 | 30 | 15 | 0 | 0 |
2019-02-01 02:15:00 | 15.006963 | -5.453942 | 0.75 | 0 | 3.037083 | 30 | 15 | 11.48629 | 0 |
2019-02-01 02:30:00 | 14.97872 | -5.371927 | 0.75 | 0 | 3.062083 | 30 | 15 | 73.341857 | 0 |
2019-02-01 02:45:00 | 14.98425 | -5.316388 | 0.75 | 0 | 3.087083 | 30 | 15 | 125.706344 | 0 |
2019-02-01 03:00:00 | 14.985159 | -5.295379 | 0.75 | 0 | 3.117294 | 30 | 15 | 178.03881 | 0 |
2019-02-01 03:15:00 | 14.986237 | -5.301252 | 0.75 | 0 | 3.168543 | 30 | 15 | 229.123202 | 0 |
2019-02-01 03:30:00 | 14.987154 | -5.328017 | 0.75 | 0 | 3.229478 | 30 | 15 | 279.115183 | 0 |
2019-02-01 03:45:00 | 14.987994 | -5.372104 | 0.75 | 0 | 3.281352 | 30 | 15 | 327.883127 | 0 |
2019-02-01 04:00:00 | 14.988724 | -5.432168 | 0.749919 | 0 | 3.312083 | 30 | 15 | 375.348621 | 0 |
2019-02-01 04:15:00 | 14.989166 | -5.513895 | 0.74957 | 0 | 3.337083 | 30 | 15 | 422.214196 | 0 |
2019-02-01 04:30:00 | 14.989578 | -5.61587 | 0.749292 | 0 | 3.362083 | 30 | 15 | 468.845708 | 0 |
2019-02-01 04:45:00 | 14.990005 | -5.734185 | 0.749552 | 0 | 3.387083 | 30 | 15 | 515.015228 | 0 |
2019-02-01 05:00:00 | 14.990326 | -5.88419 | 0.750784 | 0 | 3.412083 | 30 | 15 | 560.605507 | 0 |
2019-02-01 05:15:00 | 14.990173 | -6.119105 | 0.752997 | 0 | 3.437083 | 30 | 15 | 607.628776 | 0 |
2019-02-01 05:30:00 | 14.990486 | -6.389293 | 0.755766 | 0 | 3.462083 | 30 | 15 | 655.392045 | 0 |
2019-02-01 05:45:00 | 14.991397 | -6.617411 | 0.75862 | 0 | 3.487083 | 30 | 15 | 700.813442 | 0 |
2019-02-01 06:00:00 | 14.992849 | -6.73668 | 0.761208 | 0 | 3.483318 | 30 | 15 | 740.605034 | 0 |
2019-02-01 06:15:00 | 14.994384 | -6.755923 | 0.763708 | 0 | 3.354159 | 30 | 15 | 772.958439 | 0 |
2019-02-01 06:30:00 | 14.996067 | -6.726563 | 0.766208 | 19.90464 | 3.136249 | 30 | 15 | 797.755246 | 0 |
2019-02-01 06:45:00 | 15.003775 | -6.699575 | 0.768708 | 123.979261 | 2.904589 | 30 | 15 | 797.251898 | 0 |
2019-02-01 07:00:00 | 15.021205 | -6.788932 | 0.771208 | 279.569632 | 2.715417 | 30 | 15 | 718.475603 | 0 |
2019-02-01 07:15:00 | 15.028977 | -7.162808 | 0.773708 | 426.780753 | 2.540417 | 30 | 15 | 584.622053 | 0 |
2019-02-01 07:30:00 | 15.024342 | -7.526908 | 0.776208 | 523.674897 | 2.365417 | 30 | 15 | 457.849785 | 0 |
2019-02-01 07:45:00 | 15.02691 | -7.477521 | 0.778708 | 603.863913 | 2.190417 | 30 | 15 | 355.371607 | 0 |
2019-02-01 08:00:00 | 20.461326 | -6.789167 | 0.775047 | 677.716683 | 2.049905 | 24 | 21 | 2,938.685364 | 0 |
2019-02-01 08:15:00 | 21.056774 | -5.939167 | 0.744952 | 740.591562 | 2.06126 | 24 | 21 | 1,600.573191 | 0 |
2019-02-01 08:30:00 | 21.049473 | -5.089167 | 0.697463 | 790.497617 | 2.184832 | 24 | 21 | 1,282.812896 | 0 |
2019-02-01 08:45:00 | 21.04516 | -4.239167 | 0.649947 | 834.446315 | 2.335392 | 24 | 21 | 1,025.543473 | 0 |
2019-02-01 09:00:00 | 21.039344 | -3.389167 | 0.614856 | 872.134183 | 2.448369 | 24 | 21 | 823.237703 | 0 |
2019-02-01 09:15:00 | 21.048181 | -2.539167 | 0.583537 | 900.463281 | 2.551338 | 24 | 21 | 628.534191 | 0 |
2019-02-01 09:30:00 | 21.053643 | -1.689167 | 0.552956 | 918.489942 | 2.654611 | 24 | 21 | 435.072311 | 0 |
2019-02-01 09:45:00 | 21.068511 | -0.839167 | 0.524073 | 932.856558 | 2.752651 | 24 | 21 | 254.237978 | 0 |
2019-02-01 10:00:00 | 21.133382 | 0.016565 | 0.498094 | 944.747829 | 2.841302 | 24 | 21 | 64.229731 | 0 |
2019-02-01 10:15:00 | 21.402321 | 0.883851 | 0.475781 | 953.149962 | 2.922656 | 24 | 21 | 0.005217 | 0 |
2019-02-01 10:30:00 | 21.936447 | 1.7177 | 0.454922 | 957.894614 | 2.99776 | 24 | 21 | 0 | 0 |
2019-02-01 10:45:00 | 22.525739 | 2.461861 | 0.432703 | 961.701636 | 3.066614 | 24 | 21 | 0 | 0 |
2019-02-01 11:00:00 | 23.1314 | 3.09 | 0.406482 | 964.988223 | 3.129218 | 24 | 21 | 0 | 0 |
2019-02-01 11:15:00 | 23.74774 | 3.69 | 0.376086 | 967.28481 | 3.185572 | 24 | 21 | 0.044356 | 0 |
2019-02-01 11:30:00 | 24.242739 | 4.29 | 0.344278 | 968.346446 | 3.235677 | 24 | 21 | 38.586945 | 0 |
2019-02-01 11:45:00 | 24.125235 | 4.89 | 0.314154 | 968.793935 | 3.279531 | 24 | 21 | 153.733603 | 0 |
2019-02-01 12:00:00 | 24.067064 | 5.49 | 0.288018 | 968.814184 | 3.316125 | 24 | 21 | 223.483341 | 0 |
2019-02-01 12:15:00 | 24.055742 | 6.09 | 0.26445 | 968.385011 | 3.343118 | 24 | 21 | 292.806036 | 0 |
2019-02-01 12:30:00 | 24.045997 | 6.69 | 0.24229 | 967.279345 | 3.36474 | 24 | 21 | 356.403467 | 0 |
2019-02-01 12:45:00 | 24.038407 | 7.29 | 0.220924 | 964.802693 | 3.38685 | 24 | 21 | 414.63072 | 0 |
2019-02-01 13:00:00 | 24.030824 | 7.89 | 0.199015 | 961.238701 | 3.428047 | 24 | 21 | 465.202929 | 0 |
2019-02-01 13:15:00 | 24.026751 | 8.49 | 0.175099 | 957.148514 | 3.521881 | 24 | 21 | 510.166173 | 0 |
2019-02-01 13:30:00 | 24.023334 | 9.09 | 0.153921 | 952.341157 | 3.615579 | 24 | 21 | 551.385964 | 0 |
2019-02-01 13:45:00 | 24.019466 | 9.69 | 0.141619 | 944.280263 | 3.635312 | 24 | 21 | 587.639077 | 0 |
2019-02-01 14:00:00 | 24.015099 | 10.180568 | 0.141208 | 932.882573 | 3.539583 | 24 | 21 | 617.486475 | 0 |
2019-02-01 14:15:00 | 24.009902 | 10.195716 | 0.143708 | 918.913995 | 3.414583 | 24 | 21 | 638.704928 | 0 |
2019-02-01 14:30:00 | 24.005819 | 9.879412 | 0.146208 | 901.033617 | 3.289583 | 24 | 21 | 651.783612 | 0 |
2019-02-01 14:45:00 | 24.002397 | 9.521979 | 0.148708 | 872.001172 | 3.164583 | 24 | 21 | 658.259933 | 0 |
2019-02-01 15:00:00 | 23.999446 | 9.339367 | 0.151208 | 832.659503 | 3.039583 | 24 | 21 | 659.026078 | 0 |
2019-02-01 15:15:00 | 23.995775 | 9.214432 | 0.153708 | 786.410626 | 2.914583 | 24 | 21 | 653.311622 | 0 |
2019-02-01 15:30:00 | 23.992405 | 9.089804 | 0.156208 | 734.321367 | 2.789583 | 24 | 21 | 641.024958 | 0 |
2019-02-01 15:45:00 | 23.988968 | 8.964846 | 0.158708 | 670.264248 | 2.664583 | 24 | 21 | 622.960477 | 0 |
2019-02-01 16:00:00 | 23.983982 | 8.857491 | 0.161208 | 595.886778 | 2.531507 | 24 | 21 | 596.695008 | 0 |
2019-02-01 16:15:00 | 23.980131 | 8.807224 | 0.163708 | 515.496582 | 2.371615 | 24 | 21 | 563.671324 | 0 |
2019-02-01 16:30:00 | 23.976087 | 8.727274 | 0.166208 | 420.361184 | 2.218755 | 24 | 21 | 525.231453 | 0 |
2019-02-01 16:45:00 | 23.969209 | 8.50012 | 0.168708 | 277.31016 | 2.119801 | 24 | 21 | 478.724748 | 0 |
2019-02-01 17:00:00 | 23.962661 | 8.017392 | 0.173101 | 125.02267 | 2.117978 | 24 | 21 | 423.717997 | 0 |
2019-02-01 17:15:00 | 23.960526 | 7.276476 | 0.186021 | 21.268966 | 2.214328 | 24 | 21 | 366.834286 | 0 |
2019-02-01 17:30:00 | 23.963622 | 6.384439 | 0.205828 | 0 | 2.366147 | 24 | 21 | 316.228934 | 0 |
2019-02-01 17:45:00 | 23.966283 | 5.458803 | 0.22843 | 0 | 2.526559 | 24 | 21 | 273.430625 | 0 |
2019-02-01 18:00:00 | 24.426429 | 4.589412 | 0.250753 | 0 | 2.660417 | 30 | 15 | 8.460533 | 0 |
2019-02-01 18:15:00 | 24.559054 | 3.739368 | 0.274224 | 0 | 2.785417 | 30 | 15 | 0 | 0 |
2019-02-01 18:30:00 | 24.321177 | 2.888977 | 0.299358 | 0 | 2.910417 | 30 | 15 | 0 | 0 |
2019-02-01 18:45:00 | 24.026583 | 2.03891 | 0.325882 | 0 | 3.035417 | 30 | 15 | 0 | 0 |
2019-02-01 19:00:00 | 23.723859 | 1.178528 | 0.356271 | 0 | 3.160417 | 30 | 15 | 0 | 0 |
2019-02-01 19:15:00 | 23.422726 | 0.285198 | 0.397917 | 0 | 3.285417 | 30 | 15 | 0 | 0 |
2019-02-01 19:30:00 | 23.125614 | -0.584779 | 0.442632 | 0 | 3.410417 | 30 | 15 | 0 | 0 |
2019-02-01 19:45:00 | 22.836565 | -1.356066 | 0.478139 | 0 | 3.535417 | 30 | 15 | 0 | 0 |
2019-02-01 20:00:00 | 22.562658 | -1.922048 | 0.496241 | 0 | 3.652149 | 30 | 15 | 0 | 0 |
2019-02-01 20:15:00 | 22.316565 | -2.161232 | 0.506652 | 0 | 3.733956 | 30 | 15 | 0 | 0 |
2019-02-01 20:30:00 | 22.099672 | -2.203541 | 0.515158 | 0 | 3.794514 | 30 | 15 | 0 | 0 |
2019-02-01 20:45:00 | 21.898832 | -2.236475 | 0.524337 | 0 | 3.858822 | 30 | 15 | 0 | 0 |
2019-02-01 21:00:00 | 21.697975 | -2.396667 | 0.535991 | 0 | 3.945735 | 30 | 15 | 0 | 0 |
2019-02-01 21:15:00 | 21.492924 | -2.596667 | 0.548531 | 0 | 4.044822 | 30 | 15 | 0 | 0 |
2019-02-01 21:30:00 | 21.288161 | -2.796667 | 0.561145 | 0 | 4.147406 | 30 | 15 | 0 | 0 |
2019-02-01 21:45:00 | 21.084882 | -2.996667 | 0.573639 | 0 | 4.249022 | 30 | 15 | 0 | 0 |
2019-02-01 22:00:00 | 20.883021 | -3.200467 | 0.586707 | 0 | 4.348371 | 30 | 15 | 0 | 0 |
2019-02-01 22:15:00 | 20.681687 | -3.416887 | 0.602093 | 0 | 4.449217 | 30 | 15 | 0 | 0 |
2019-02-01 22:30:00 | 20.480999 | -3.629998 | 0.615524 | 0 | 4.536336 | 30 | 15 | 0 | 0 |
2019-02-01 22:45:00 | 20.282705 | -3.817741 | 0.621258 | 0 | 4.589638 | 30 | 15 | 0 | 0 |
2019-02-01 23:00:00 | 20.089134 | -3.958717 | 0.616375 | 0 | 4.590598 | 30 | 15 | 0 | 0 |
2019-02-01 23:15:00 | 19.902122 | -4.048885 | 0.608875 | 0 | 4.538769 | 30 | 15 | 0 | 0 |
2019-02-01 23:30:00 | 19.721204 | -4.109687 | 0.601375 | 0 | 4.452454 | 30 | 15 | 0 | 0 |
2019-02-01 23:45:00 | 19.544366 | -4.165939 | 0.593875 | 0 | 4.351742 | 30 | 15 | 0 | 0 |
2019-02-02 00:00:00 | 19.369239 | -4.23625 | 0.586375 | 0 | 4.252475 | 30 | 15 | 0 | 0 |
2019-02-02 00:15:00 | 19.194972 | -4.31125 | 0.578875 | 0 | 4.151549 | 30 | 15 | 0 | 0 |
2019-02-02 00:30:00 | 19.021978 | -4.38625 | 0.571375 | 0 | 4.04939 | 30 | 15 | 0 | 0 |
2019-02-02 00:45:00 | 18.850312 | -4.46125 | 0.563875 | 0 | 3.949681 | 30 | 15 | 0 | 0 |
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TTM4HVAC – Training dataset (target-default)
This dataset contains target-building time-series data under default HVAC operation profiles.
It is used to train or fine-tune the model gft/ttm4hvac-target-default.
Check out the paper arXiv:XXXX.XXXXX (to be released) and visit the main repository ttm4hvac for further details.
Columns
timeRoom Air Temperature (C)Outdoor Air Temperature (C)Outdoor Humidity (%)Direct Solar Radiation (W/m^2)Wind Speed (m/s)Cooling Setpoint (C)Heating Setpoint (C)HVAC Power Consumption (W)series_id
Usage
from datasets import load_dataset
ds = load_dataset("gft/ttm4hvac-target-default-train")
df = ds["train"].to_pandas()
df.head()
✒️ 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
- Downloads last month
- 15
Size of downloaded dataset files:
1.54 MB
Size of the auto-converted Parquet files:
1.54 MB
Number of rows:
29,088
Models trained or fine-tuned on gft/ttm4hvac-target-default-train