gft/ttm4hvac-target-default
Time Series Forecasting
•
943k
•
Updated
•
5
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
|
End of preview. Expand
in Data Studio
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
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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