gft/ttm4hvac-target-chaotic
Time Series Forecasting
•
869k
•
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
| 22.497412
| -7.888248
| 0.75
| 0
| 2.708807
| 35
| 22.5
| 1,684.177509
| 0
|
2019-02-01 00:15:00
| 22.497892
| -7.42054
| 0.75
| 0
| 2.754325
| 35
| 22.5
| 1,699.412678
| 0
|
2019-02-01 00:30:00
| 22.498178
| -6.942734
| 0.75
| 0
| 2.820624
| 35
| 22.5
| 1,712.306172
| 0
|
2019-02-01 00:45:00
| 22.498244
| -6.502096
| 0.75
| 0
| 2.87958
| 35
| 22.5
| 1,724.301356
| 0
|
2019-02-01 01:00:00
| 22.498043
| -6.148435
| 0.75
| 0
| 2.912083
| 35
| 22.5
| 1,737.064599
| 0
|
2019-02-01 01:15:00
| 22.497671
| -5.910945
| 0.75
| 0
| 2.937083
| 35
| 22.5
| 1,752.248662
| 0
|
2019-02-01 01:30:00
| 22.497435
| -5.758935
| 0.75
| 0
| 2.962083
| 35
| 22.5
| 1,769.536073
| 0
|
2019-02-01 01:45:00
| 22.497419
| -5.651153
| 0.75
| 0
| 2.987083
| 35
| 22.5
| 1,787.541559
| 0
|
2019-02-01 02:00:00
| 22.497664
| -5.551004
| 0.75
| 0
| 3.012083
| 35
| 22.5
| 1,804.592882
| 0
|
2019-02-01 02:15:00
| 22.497953
| -5.453942
| 0.75
| 0
| 3.037083
| 35
| 22.5
| 1,819.200118
| 0
|
2019-02-01 02:30:00
| 22.497694
| -5.371927
| 0.75
| 0
| 3.062083
| 35
| 22.5
| 1,834.825742
| 0
|
2019-02-01 02:45:00
| 22.497455
| -5.316388
| 0.75
| 0
| 3.087083
| 35
| 22.5
| 1,852.224975
| 0
|
2019-02-01 03:00:00
| 22.497199
| -5.295379
| 0.75
| 0
| 3.117294
| 35
| 22.5
| 1,871.502962
| 0
|
2019-02-01 03:15:00
| 22.496985
| -5.301252
| 0.75
| 0
| 3.168543
| 35
| 22.5
| 1,892.522482
| 0
|
2019-02-01 03:30:00
| 19.855396
| -5.328017
| 0.75
| 0
| 3.229478
| 31.325726
| 16.13068
| 63.475222
| 0
|
2019-02-01 03:45:00
| 20.575083
| -5.372104
| 0.75
| 0
| 3.281352
| 25.721196
| 21.061584
| 1,780.196968
| 0
|
2019-02-01 04:00:00
| 19.031654
| -5.432168
| 0.749919
| 0
| 3.312083
| 29.712755
| 12.764345
| 47.631888
| 0
|
2019-02-01 04:15:00
| 19.684207
| -5.513895
| 0.74957
| 0
| 3.337083
| 25.863997
| 20.039164
| 1,444.250671
| 0
|
2019-02-01 04:30:00
| 22.124297
| -5.61587
| 0.749292
| 0
| 3.362083
| 23.05182
| 22.401152
| 2,530.780971
| 0
|
2019-02-01 04:45:00
| 20.235309
| -5.734185
| 0.749552
| 0
| 3.387083
| 24.129396
| 19.950857
| 760.923739
| 0
|
2019-02-01 05:00:00
| 18.225101
| -5.88419
| 0.750784
| 0
| 3.412083
| 27.434691
| 16.152702
| 41.976059
| 0
|
2019-02-01 05:15:00
| 16.916655
| -6.119105
| 0.752997
| 0
| 3.437083
| 33.508132
| 15.16139
| 0
| 0
|
2019-02-01 05:30:00
| 16.532494
| -6.389293
| 0.755766
| 0
| 3.462083
| 29.218672
| 10.616246
| 0
| 0
|
2019-02-01 05:45:00
| 16.287107
| -6.617411
| 0.75862
| 0
| 3.487083
| 22.529343
| 13.059998
| 0
| 0
|
2019-02-01 06:00:00
| 16.078268
| -6.73668
| 0.761208
| 0
| 3.483318
| 31.007289
| 11.515811
| 0
| 0
|
2019-02-01 06:15:00
| 15.892411
| -6.755923
| 0.763708
| 0
| 3.354159
| 23.922024
| 15.137008
| 0
| 0
|
2019-02-01 06:30:00
| 18.478615
| -6.726563
| 0.766208
| 19.90464
| 3.136249
| 30.597963
| 18.9009
| 1,778.492814
| 0
|
2019-02-01 06:45:00
| 17.118669
| -6.699575
| 0.768708
| 123.979261
| 2.904589
| 24.730633
| 10.994852
| 42.659019
| 0
|
2019-02-01 07:00:00
| 15.924563
| -6.788932
| 0.771208
| 279.569632
| 2.715417
| 34.254963
| 13.25833
| 0
| 0
|
2019-02-01 07:15:00
| 20.238332
| -7.162808
| 0.773708
| 426.780753
| 2.540417
| 33.166831
| 20.793643
| 2,602.12848
| 0
|
2019-02-01 07:30:00
| 18.536468
| -7.526908
| 0.776208
| 523.674897
| 2.365417
| 32.156532
| 12.11042
| 55.553505
| 0
|
2019-02-01 07:45:00
| 19.322742
| -7.477521
| 0.778708
| 603.863913
| 2.190417
| 32.772518
| 19.615632
| 1,339.474275
| 0
|
2019-02-01 08:00:00
| 19.07566
| -6.789167
| 0.775047
| 677.716683
| 2.049905
| 29.959037
| 14.215025
| 27.766824
| 0
|
2019-02-01 08:15:00
| 18.891868
| -5.939167
| 0.744952
| 740.591562
| 2.06126
| 25.781358
| 11.523615
| 0
| 0
|
2019-02-01 08:30:00
| 19.27332
| -5.089167
| 0.697463
| 790.497617
| 2.184832
| 28.871963
| 17.315761
| 0
| 0
|
2019-02-01 08:45:00
| 19.772618
| -4.239167
| 0.649947
| 834.446315
| 2.335392
| 26.991737
| 15.941988
| 0
| 0
|
2019-02-01 09:00:00
| 20.310416
| -3.389167
| 0.614856
| 872.134183
| 2.448369
| 27.043931
| 18.012576
| 0
| 0
|
2019-02-01 09:15:00
| 20.884519
| -2.539167
| 0.583537
| 900.463281
| 2.551338
| 33.76574
| 12.049003
| 0
| 0
|
2019-02-01 09:30:00
| 21.483223
| -1.689167
| 0.552956
| 918.489942
| 2.654611
| 27.26761
| 11.760858
| 0
| 0
|
2019-02-01 09:45:00
| 22.085571
| -0.839167
| 0.524073
| 932.856558
| 2.752651
| 30.311797
| 19.110881
| 0
| 0
|
2019-02-01 10:00:00
| 22.71328
| 0.016565
| 0.498094
| 944.747829
| 2.841302
| 34.148909
| 13.485899
| 0
| 0
|
2019-02-01 10:15:00
| 23.353806
| 0.883851
| 0.475781
| 953.149962
| 2.922656
| 34.37764
| 10.790783
| 0
| 0
|
2019-02-01 10:30:00
| 23.986406
| 1.7177
| 0.454922
| 957.894614
| 2.99776
| 30.037673
| 10.714159
| 0
| 0
|
2019-02-01 10:45:00
| 24.606527
| 2.461861
| 0.432703
| 961.701636
| 3.066614
| 33.672177
| 16.622613
| 0
| 0
|
2019-02-01 11:00:00
| 25.225042
| 3.09
| 0.406482
| 964.988223
| 3.129218
| 30.866854
| 20.394674
| 0
| 0
|
2019-02-01 11:15:00
| 24.759918
| 3.69
| 0.376086
| 967.28481
| 3.185572
| 24.378883
| 10.482395
| 247.274432
| 0
|
2019-02-01 11:30:00
| 25.666525
| 4.29
| 0.344278
| 968.346446
| 3.235677
| 33.486014
| 13.936665
| 8.750449
| 0
|
2019-02-01 11:45:00
| 24.637452
| 4.89
| 0.314154
| 968.793935
| 3.279531
| 24.112236
| 15.855765
| 486.832349
| 0
|
2019-02-01 12:00:00
| 26.138965
| 5.49
| 0.288018
| 968.814184
| 3.316125
| 29.978826
| 10.387133
| 14.455418
| 0
|
2019-02-01 12:15:00
| 26.338021
| 6.09
| 0.26445
| 968.385011
| 3.343118
| 25.936196
| 10.409998
| 329.139859
| 0
|
2019-02-01 12:30:00
| 27.385143
| 6.69
| 0.24229
| 967.279345
| 3.36474
| 27.754192
| 21.18875
| 40.648537
| 0
|
2019-02-01 12:45:00
| 28.636124
| 7.29
| 0.220924
| 964.802693
| 3.38685
| 34.783155
| 17.963094
| 4.08616
| 0
|
2019-02-01 13:00:00
| 25.234779
| 7.89
| 0.199015
| 961.238701
| 3.428047
| 24.520471
| 21.323117
| 872.746294
| 0
|
2019-02-01 13:15:00
| 27.576245
| 8.49
| 0.175099
| 957.148514
| 3.521881
| 28.953098
| 11.921866
| 27.189993
| 0
|
2019-02-01 13:30:00
| 29.715289
| 9.09
| 0.153921
| 952.341157
| 3.615579
| 34.021583
| 19.468363
| 1.65637
| 0
|
2019-02-01 13:45:00
| 30.320521
| 9.69
| 0.141619
| 944.280263
| 3.635312
| 31.646316
| 18.864759
| 0
| 0
|
2019-02-01 14:00:00
| 25.836197
| 10.180568
| 0.141208
| 932.882573
| 3.539583
| 25.004248
| 12.637584
| 1,047.396651
| 0
|
2019-02-01 14:15:00
| 28.476537
| 10.195716
| 0.143708
| 918.913995
| 3.414583
| 31.103662
| 12.746534
| 27.637071
| 0
|
2019-02-01 14:30:00
| 28.414235
| 9.879412
| 0.146208
| 901.033617
| 3.289583
| 27.983396
| 19.775023
| 503.227505
| 0
|
2019-02-01 14:45:00
| 29.840746
| 9.521979
| 0.148708
| 872.001172
| 3.164583
| 32.299406
| 15.665772
| 14.016722
| 0
|
2019-02-01 15:00:00
| 31.084679
| 9.339367
| 0.151208
| 832.659503
| 3.039583
| 32.402374
| 20.482772
| 0
| 0
|
2019-02-01 15:15:00
| 30.774605
| 9.214432
| 0.153708
| 786.410626
| 2.914583
| 30.684697
| 12.855323
| 118.269878
| 0
|
2019-02-01 15:30:00
| 26.579405
| 9.089804
| 0.156208
| 734.321367
| 2.789583
| 25.925147
| 13.935514
| 956.704217
| 0
|
2019-02-01 15:45:00
| 24.221792
| 8.964846
| 0.158708
| 670.264248
| 2.664583
| 23.960051
| 13.414495
| 1,138.984249
| 0
|
2019-02-01 16:00:00
| 24.916187
| 8.857491
| 0.161208
| 595.886778
| 2.531507
| 25.11899
| 15.691609
| 760.132328
| 0
|
2019-02-01 16:15:00
| 28.23214
| 8.807224
| 0.163708
| 515.496582
| 2.371615
| 33.855469
| 11.306004
| 25.811825
| 0
|
2019-02-01 16:30:00
| 30.121177
| 8.727274
| 0.166208
| 420.361184
| 2.218755
| 31.503349
| 18.591038
| 0
| 0
|
2019-02-01 16:45:00
| 24.653052
| 8.50012
| 0.168708
| 277.31016
| 2.119801
| 23.696927
| 21.532155
| 1,195.631378
| 0
|
2019-02-01 17:00:00
| 27.096643
| 8.017392
| 0.173101
| 125.02267
| 2.117978
| 29.605903
| 14.546569
| 30.090042
| 0
|
2019-02-01 17:15:00
| 29.073927
| 7.276476
| 0.186021
| 21.268966
| 2.214328
| 31.956732
| 13.217068
| 0
| 0
|
2019-02-01 17:30:00
| 29.067206
| 6.384439
| 0.205828
| 0
| 2.366147
| 31.168873
| 10.49639
| 0
| 0
|
2019-02-01 17:45:00
| 28.840348
| 5.458803
| 0.22843
| 0
| 2.526559
| 32.9926
| 15.602136
| 0
| 0
|
2019-02-01 18:00:00
| 27.983326
| 4.589412
| 0.250753
| 0
| 2.660417
| 30.458661
| 12.765473
| 0
| 0
|
2019-02-01 18:15:00
| 27.230958
| 3.739368
| 0.274224
| 0
| 2.785417
| 30.963421
| 19.653003
| 0
| 0
|
2019-02-01 18:30:00
| 26.81103
| 2.888977
| 0.299358
| 0
| 2.910417
| 28.415165
| 17.721587
| 0
| 0
|
2019-02-01 18:45:00
| 26.454816
| 2.03891
| 0.325882
| 0
| 3.035417
| 28.204074
| 21.176142
| 0
| 0
|
2019-02-01 19:00:00
| 26.11644
| 1.178528
| 0.356271
| 0
| 3.160417
| 28.723365
| 20.522609
| 0
| 0
|
2019-02-01 19:15:00
| 25.788625
| 0.285198
| 0.397917
| 0
| 3.285417
| 32.516901
| 15.13131
| 0
| 0
|
2019-02-01 19:30:00
| 25.264889
| -0.584779
| 0.442632
| 0
| 3.410417
| 25.376056
| 15.076027
| 31.306105
| 0
|
2019-02-01 19:45:00
| 25.096503
| -1.356066
| 0.478139
| 0
| 3.535417
| 32.827717
| 12.845407
| 0
| 0
|
2019-02-01 20:00:00
| 24.847905
| -1.922048
| 0.496241
| 0
| 3.652149
| 29.055454
| 20.076209
| 0
| 0
|
2019-02-01 20:15:00
| 24.588922
| -2.161232
| 0.506652
| 0
| 3.733956
| 26.918468
| 18.02762
| 0
| 0
|
2019-02-01 20:30:00
| 24.354327
| -2.203541
| 0.515158
| 0
| 3.794514
| 25.570395
| 11.365993
| 0
| 0
|
2019-02-01 20:45:00
| 24.13529
| -2.236475
| 0.524337
| 0
| 3.858822
| 32.429803
| 11.237108
| 0
| 0
|
2019-02-01 21:00:00
| 23.916505
| -2.396667
| 0.535991
| 0
| 3.945735
| 25.892267
| 20.688868
| 0
| 0
|
2019-02-01 21:15:00
| 23.693963
| -2.596667
| 0.548531
| 0
| 4.044822
| 26.830866
| 22.163067
| 0
| 0
|
2019-02-01 21:30:00
| 23.472106
| -2.796667
| 0.561145
| 0
| 4.147406
| 34.746679
| 13.35345
| 0
| 0
|
2019-02-01 21:45:00
| 23.003413
| -2.996667
| 0.573639
| 0
| 4.249022
| 23.012228
| 18.813291
| 49.845752
| 0
|
2019-02-01 22:00:00
| 22.898181
| -3.200467
| 0.586707
| 0
| 4.348371
| 28.137331
| 11.259411
| 0.957937
| 0
|
2019-02-01 22:15:00
| 22.788976
| -3.416887
| 0.602093
| 0
| 4.449217
| 31.344035
| 10.389686
| 0
| 0
|
2019-02-01 22:30:00
| 22.585744
| -3.629998
| 0.615524
| 0
| 4.536336
| 33.377459
| 11.733157
| 0
| 0
|
2019-02-01 22:45:00
| 22.374573
| -3.817741
| 0.621258
| 0
| 4.589638
| 24.091398
| 12.195904
| 0
| 0
|
2019-02-01 23:00:00
| 22.1667
| -3.958717
| 0.616375
| 0
| 4.590598
| 34.503329
| 18.664617
| 0
| 0
|
2019-02-01 23:15:00
| 21.965176
| -4.048885
| 0.608875
| 0
| 4.538769
| 32.517144
| 14.970437
| 0
| 0
|
2019-02-01 23:30:00
| 21.769782
| -4.109687
| 0.601375
| 0
| 4.452454
| 26.703909
| 12.93105
| 0
| 0
|
2019-02-01 23:45:00
| 21.578578
| -4.165939
| 0.593875
| 0
| 4.351742
| 28.5081
| 16.897859
| 0
| 0
|
2019-02-02 00:00:00
| 21.389226
| -4.23625
| 0.586375
| 0
| 4.252475
| 30.397186
| 13.213981
| 0
| 0
|
2019-02-02 00:15:00
| 21.200889
| -4.31125
| 0.578875
| 0
| 4.151549
| 34.358176
| 12.121981
| 0
| 0
|
2019-02-02 00:30:00
| 21.013985
| -4.38625
| 0.571375
| 0
| 4.04939
| 34.570665
| 10.185702
| 0
| 0
|
2019-02-02 00:45:00
| 20.828563
| -4.46125
| 0.563875
| 0
| 3.949681
| 28.514071
| 14.167098
| 0
| 0
|
End of preview. Expand
in Data Studio
TTM4HVAC – Training dataset (target-chaotic)
This dataset contains target-building time-series data generated under chaotic control policies, designed to stress-test generalization.
It was used to train the model gft/ttm4hvac-target-chaotic.
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-chaotic-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
- 11
Size of downloaded dataset files:
2.03 MB
Size of the auto-converted Parquet files:
2.03 MB
Number of rows:
29,088
Models trained or fine-tuned on gft/ttm4hvac-target-chaotic-train