gft/ttm4hvac-source-default
Time Series Forecasting
•
3.12M
•
Updated
•
8
Datasets:
time
stringdate 2019-01-01 00:00:00
2019-12-31 23:45:00
| Outdoor Air Temperature (C)
float64 -13.9
42
| Heating Setpoint (C)
float64 12.8
21.1
| Cooling Setpoint (C)
float64 23.9
40
| Room Air Temperature (C)
float64 8.82
32.4
| Outdoor Humidity (%)
float64 3
100
| Wind Speed (m/s)
float64 0
17.5
| Direct Solar Radiation (W/m^2)
float64 0
1.03k
| HVAC Power Consumption (W)
float64 20
717k
| series_id
int64 1
29
| is_default
bool 1
class |
|---|---|---|---|---|---|---|---|---|---|---|
2019-01-01 00:00:00
| 3.15
| 12.8
| 40
| 18.945633
| 93
| 0.65
| 0
| 96.85838
| 1
| true
|
2019-01-01 00:15:00
| 4.075
| 12.8
| 40
| 18.855467
| 93.5
| 0.325
| 0
| 127.47386
| 1
| true
|
2019-01-01 00:30:00
| 5
| 12.8
| 40
| 18.773378
| 94
| 0
| 0
| 156.1336
| 1
| true
|
2019-01-01 00:45:00
| 5.125
| 12.8
| 40
| 18.69623
| 93.75
| 0
| 0
| 183.73726
| 1
| true
|
2019-01-01 01:00:00
| 5.25
| 12.8
| 40
| 18.624104
| 93.5
| 0
| 0
| 209.07198
| 1
| true
|
2019-01-01 01:15:00
| 5.375
| 12.8
| 40
| 18.557585
| 93.25
| 0
| 0
| 232.19963
| 1
| true
|
2019-01-01 01:30:00
| 5.5
| 12.8
| 40
| 18.49672
| 93
| 0
| 0
| 253.11432
| 1
| true
|
2019-01-01 01:45:00
| 5.625
| 12.8
| 40
| 18.44141
| 93.5
| 0
| 0
| 272.01675
| 1
| true
|
2019-01-01 02:00:00
| 5.75
| 12.8
| 40
| 18.390991
| 94
| 0
| 0
| 289.03534
| 1
| true
|
2019-01-01 02:15:00
| 5.875
| 12.8
| 40
| 18.345003
| 94.5
| 0
| 0
| 304.4274
| 1
| true
|
2019-01-01 02:30:00
| 6
| 12.8
| 40
| 18.303108
| 95
| 0
| 0
| 318.37735
| 1
| true
|
2019-01-01 02:45:00
| 6.125
| 12.8
| 40
| 18.265
| 94.5
| 0
| 0
| 331.03082
| 1
| true
|
2019-01-01 03:00:00
| 6.25
| 12.8
| 40
| 18.230371
| 94
| 0
| 0
| 342.5208
| 1
| true
|
2019-01-01 03:15:00
| 6.375
| 12.8
| 40
| 18.198927
| 93.5
| 0
| 0
| 352.96277
| 1
| true
|
2019-01-01 03:30:00
| 6.5
| 12.8
| 40
| 18.170658
| 93
| 0
| 0
| 362.45117
| 1
| true
|
2019-01-01 03:45:00
| 6.625
| 12.8
| 40
| 18.14507
| 93
| 0
| 0
| 371.00098
| 1
| true
|
2019-01-01 04:00:00
| 6.75
| 12.8
| 40
| 18.121944
| 93
| 0
| 0
| 378.42584
| 1
| true
|
2019-01-01 04:15:00
| 6.875
| 12.8
| 40
| 18.101124
| 93
| 0
| 0
| 385.20135
| 1
| true
|
2019-01-01 04:30:00
| 7
| 12.8
| 40
| 18.082827
| 93
| 0
| 0
| 391.35052
| 1
| true
|
2019-01-01 04:45:00
| 6.75
| 12.8
| 40
| 18.061968
| 94.75
| 0.65
| 0
| 396.8069
| 1
| true
|
2019-01-01 05:00:00
| 6.5
| 12.8
| 40
| 18.038364
| 96.5
| 1.3
| 0
| 401.86243
| 1
| true
|
2019-01-01 05:15:00
| 6.25
| 12.8
| 40
| 18.01295
| 98.25
| 1.95
| 0
| 406.6963
| 1
| true
|
2019-01-01 05:30:00
| 6
| 12.8
| 40
| 17.985691
| 100
| 2.6
| 0
| 411.37622
| 1
| true
|
2019-01-01 05:45:00
| 6
| 12.8
| 40
| 17.96402
| 96.75
| 2.6
| 0
| 920.3171
| 1
| true
|
2019-01-01 06:00:00
| 6
| 12.8
| 40
| 17.943827
| 93.5
| 2.6
| 0
| 909.60345
| 1
| true
|
2019-01-01 06:15:00
| 6
| 12.8
| 40
| 17.924822
| 90.25
| 2.6
| 0
| 912.0151
| 1
| true
|
2019-01-01 06:30:00
| 6
| 12.8
| 40
| 17.90695
| 87
| 2.6
| 0
| 914.22974
| 1
| true
|
2019-01-01 06:45:00
| 5.75
| 21.1
| 23.9
| 18.716099
| 88.5
| 2.6
| 0
| 12,753.002
| 1
| true
|
2019-01-01 07:00:00
| 5.5
| 21.1
| 23.9
| 19.280523
| 90
| 2.6
| 0
| 12,754.665
| 1
| true
|
2019-01-01 07:15:00
| 5.25
| 21.1
| 23.9
| 19.64331
| 91.5
| 2.6
| 0
| 12,292.305
| 1
| true
|
2019-01-01 07:30:00
| 5
| 21.1
| 23.9
| 19.930365
| 93
| 2.6
| 0
| 10,328.4795
| 1
| true
|
2019-01-01 07:45:00
| 4.75
| 21.1
| 23.9
| 21.189993
| 93
| 2.6
| 151.5
| 3,145.9268
| 1
| true
|
2019-01-01 08:00:00
| 4.5
| 21.1
| 23.9
| 21.815123
| 93
| 2.6
| 202
| 2,050.5535
| 1
| true
|
2019-01-01 08:15:00
| 4.25
| 21.1
| 23.9
| 22.330204
| 93
| 2.6
| 274
| 1,262.1376
| 1
| true
|
2019-01-01 08:30:00
| 4
| 21.1
| 23.9
| 22.75681
| 93
| 2.6
| 346
| 1,318.3138
| 1
| true
|
2019-01-01 08:45:00
| 4
| 21.1
| 23.9
| 23.560417
| 93
| 2.6
| 418
| 335.85345
| 1
| true
|
2019-01-01 09:00:00
| 4
| 21.1
| 23.9
| 23.899504
| 93
| 2.6
| 490
| 71.64491
| 1
| true
|
2019-01-01 09:15:00
| 4
| 21.1
| 23.9
| 23.899332
| 93
| 2.6
| 497
| 71.64491
| 1
| true
|
2019-01-01 09:30:00
| 4
| 21.1
| 23.9
| 23.89919
| 93
| 2.6
| 504
| 87.30448
| 1
| true
|
2019-01-01 09:45:00
| 4
| 21.1
| 23.9
| 23.89878
| 91.5
| 2.6
| 511
| 126.35662
| 1
| true
|
2019-01-01 10:00:00
| 4
| 21.1
| 23.9
| 23.898693
| 90
| 2.6
| 518
| 157.33376
| 1
| true
|
2019-01-01 10:15:00
| 4
| 21.1
| 23.9
| 23.89839
| 88.5
| 2.6
| 511.25
| 183.61783
| 1
| true
|
2019-01-01 10:30:00
| 4
| 21.1
| 23.9
| 23.898314
| 87
| 2.6
| 504.5
| 217.63326
| 1
| true
|
2019-01-01 10:45:00
| 3.75
| 21.1
| 23.9
| 23.898886
| 88.5
| 2.6
| 497.75
| 724.0713
| 1
| true
|
2019-01-01 11:00:00
| 3.5
| 21.1
| 23.9
| 23.898897
| 90
| 2.6
| 491
| 793.2132
| 1
| true
|
2019-01-01 11:15:00
| 3.25
| 21.1
| 23.9
| 23.898428
| 91.5
| 2.6
| 454.75
| 813.4544
| 1
| true
|
2019-01-01 11:30:00
| 3
| 21.1
| 23.9
| 23.898914
| 93
| 2.6
| 418.5
| 864.0884
| 1
| true
|
2019-01-01 11:45:00
| 3
| 21.1
| 23.9
| 23.899145
| 91.5
| 2.85
| 382.25
| 744.33203
| 1
| true
|
2019-01-01 12:00:00
| 3
| 21.1
| 23.9
| 23.899221
| 90
| 3.1
| 346
| 709.43097
| 1
| true
|
2019-01-01 12:15:00
| 3
| 21.1
| 23.9
| 23.898966
| 88.5
| 3.35
| 330.25
| 497.35455
| 1
| true
|
2019-01-01 12:30:00
| 3
| 21.1
| 23.9
| 23.89899
| 87
| 3.6
| 314.5
| 499.89212
| 1
| true
|
2019-01-01 12:45:00
| 3
| 21.1
| 23.9
| 23.898865
| 87
| 3.35
| 298.75
| 727.58246
| 1
| true
|
2019-01-01 13:00:00
| 3
| 21.1
| 23.9
| 23.898846
| 87
| 3.1
| 283
| 757.80975
| 1
| true
|
2019-01-01 13:15:00
| 3
| 21.1
| 23.9
| 23.898811
| 87
| 2.85
| 283.75
| 796.2606
| 1
| true
|
2019-01-01 13:30:00
| 3
| 21.1
| 23.9
| 23.898783
| 87
| 2.6
| 284.5
| 836.792
| 1
| true
|
2019-01-01 13:45:00
| 3
| 21.1
| 23.9
| 23.898706
| 87
| 2.975
| 285.25
| 889.9014
| 1
| true
|
2019-01-01 14:00:00
| 3
| 21.1
| 23.9
| 23.898642
| 87
| 3.35
| 286
| 896.3534
| 1
| true
|
2019-01-01 14:15:00
| 3
| 21.1
| 23.9
| 23.898582
| 87
| 3.725
| 271.75
| 899.80865
| 1
| true
|
2019-01-01 14:30:00
| 3
| 21.1
| 23.9
| 23.898554
| 87
| 4.1
| 257.5
| 901.9502
| 1
| true
|
2019-01-01 14:45:00
| 3.25
| 21.1
| 23.9
| 23.898546
| 82.5
| 3.85
| 243.25
| 888.05676
| 1
| true
|
2019-01-01 15:00:00
| 3.5
| 21.1
| 23.9
| 23.898468
| 78
| 3.6
| 229
| 912.09454
| 1
| true
|
2019-01-01 15:15:00
| 3.75
| 21.1
| 23.9
| 23.898392
| 73.5
| 3.35
| 171.75
| 880.36
| 1
| true
|
2019-01-01 15:30:00
| 4
| 21.1
| 23.9
| 23.89813
| 69
| 3.1
| 114.5
| 359.35916
| 1
| true
|
2019-01-01 15:45:00
| 3.75
| 21.1
| 23.9
| 23.898632
| 70.25
| 3.35
| 57.25
| 2,040.4888
| 1
| true
|
2019-01-01 16:00:00
| 3.5
| 21.1
| 23.9
| 23.898888
| 71.5
| 3.6
| 0
| 1,536.5146
| 1
| true
|
2019-01-01 16:15:00
| 3.25
| 21.1
| 23.9
| 23.899035
| 72.75
| 3.85
| 0
| 1,519.4249
| 1
| true
|
2019-01-01 16:30:00
| 3
| 21.1
| 23.9
| 23.899107
| 74
| 4.1
| 0
| 1,463.6941
| 1
| true
|
2019-01-01 16:45:00
| 3
| 21.1
| 23.9
| 23.899145
| 74
| 3.975
| 0
| 1,403.9542
| 1
| true
|
2019-01-01 17:00:00
| 3
| 21.1
| 23.9
| 23.899181
| 74
| 3.85
| 0
| 1,355.6655
| 1
| true
|
2019-01-01 17:15:00
| 3
| 21.1
| 23.9
| 23.899214
| 74
| 3.725
| 0
| 1,320.4274
| 1
| true
|
2019-01-01 17:30:00
| 3
| 21.1
| 23.9
| 23.899235
| 74
| 3.6
| 0
| 1,289.829
| 1
| true
|
2019-01-01 17:45:00
| 2.85
| 12.8
| 40
| 24.106281
| 76.25
| 3.6
| 0
| 477.3326
| 1
| true
|
2019-01-01 18:00:00
| 2.7
| 12.8
| 40
| 24.259684
| 78.5
| 3.6
| 0
| 467.06302
| 1
| true
|
2019-01-01 18:15:00
| 2.55
| 12.8
| 40
| 24.249086
| 80.75
| 3.6
| 0
| 462.35687
| 1
| true
|
2019-01-01 18:30:00
| 2.4
| 12.8
| 40
| 24.192734
| 83
| 3.6
| 0
| 456.55695
| 1
| true
|
2019-01-01 18:45:00
| 2.3
| 12.8
| 40
| 23.751898
| 85.5
| 3.6
| 0
| 365.1354
| 1
| true
|
2019-01-01 19:00:00
| 2.2
| 12.8
| 40
| 23.376877
| 88
| 3.6
| 0
| 349.01608
| 1
| true
|
2019-01-01 19:15:00
| 2.1
| 12.8
| 40
| 23.083319
| 90.5
| 3.6
| 0
| 334.96362
| 1
| true
|
2019-01-01 19:30:00
| 2
| 12.8
| 40
| 22.818445
| 93
| 3.6
| 0
| 320.89325
| 1
| true
|
2019-01-01 19:45:00
| 1.75
| 12.8
| 40
| 22.350374
| 91.25
| 2.7
| 0
| 347.5073
| 1
| true
|
2019-01-01 20:00:00
| 1.5
| 12.8
| 40
| 21.951775
| 89.5
| 1.8
| 0
| 374.1716
| 1
| true
|
2019-01-01 20:15:00
| 1.25
| 12.8
| 40
| 21.667957
| 87.75
| 0.9
| 0
| 401.80237
| 1
| true
|
2019-01-01 20:30:00
| 1
| 12.8
| 40
| 21.494053
| 86
| 0
| 0
| 439.5447
| 1
| true
|
2019-01-01 20:45:00
| 1
| 12.8
| 40
| 21.132217
| 87.75
| 0.525
| 0
| 406.21857
| 1
| true
|
2019-01-01 21:00:00
| 1
| 12.8
| 40
| 20.78174
| 89.5
| 1.05
| 0
| 392.10254
| 1
| true
|
2019-01-01 21:15:00
| 1
| 12.8
| 40
| 20.561472
| 91.25
| 1.575
| 0
| 387.606
| 1
| true
|
2019-01-01 21:30:00
| 1
| 12.8
| 40
| 20.327772
| 93
| 2.1
| 0
| 376.7731
| 1
| true
|
2019-01-01 21:45:00
| 1
| 12.8
| 40
| 20.150366
| 93
| 1.95
| 0
| 364.568
| 1
| true
|
2019-01-01 22:00:00
| 1
| 12.8
| 40
| 19.972057
| 93
| 1.8
| 0
| 352.56647
| 1
| true
|
2019-01-01 22:15:00
| 1
| 12.8
| 40
| 19.814013
| 93
| 1.65
| 0
| 340.42557
| 1
| true
|
2019-01-01 22:30:00
| 1
| 12.8
| 40
| 19.667925
| 93
| 1.5
| 0
| 328.65356
| 1
| true
|
2019-01-01 22:45:00
| 1.075
| 12.8
| 40
| 19.52856
| 92.75
| 1.45
| 0
| 302.75876
| 1
| true
|
2019-01-01 23:00:00
| 1.15
| 12.8
| 40
| 19.395695
| 92.5
| 1.4
| 0
| 276.77557
| 1
| true
|
2019-01-01 23:15:00
| 1.225
| 12.8
| 40
| 19.269901
| 92.25
| 1.35
| 0
| 250.70247
| 1
| true
|
2019-01-01 23:30:00
| 1.3
| 12.8
| 40
| 19.151215
| 92
| 1.3
| 0
| 224.58292
| 1
| true
|
2019-01-01 23:45:00
| 1.35
| 12.8
| 40
| 19.040329
| 91.75
| 1.225
| 0
| 201.64491
| 1
| true
|
2019-01-02 00:00:00
| 1.4
| 12.8
| 40
| 18.935324
| 91.5
| 1.15
| 0
| 219.50099
| 1
| true
|
2019-01-02 00:15:00
| 1.45
| 12.8
| 40
| 18.836378
| 91.25
| 1.075
| 0
| 241.85873
| 1
| true
|
2019-01-02 00:30:00
| 1.5
| 12.8
| 40
| 18.743382
| 91
| 1
| 0
| 262.84323
| 1
| true
|
2019-01-02 00:45:00
| 1.475
| 12.8
| 40
| 18.655632
| 91.25
| 0.95
| 0
| 297.59042
| 1
| true
|
End of preview. Expand
in Data Studio
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 arXiv:XXXX.XXXXX (to be released) and visit the main repository ttm4hvac for further details.
Columns
timeOutdoor 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_idis_default
Usage
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:
**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:
9.33 MB
Size of the auto-converted Parquet files:
9.33 MB
Number of rows:
525,600
Models trained or fine-tuned on gft/ttm4hvac-source-default-train