gft/ttm4hvac-source-default
Time Series Forecasting
•
3.12M
•
Updated
•
11
Datasets:
time
stringdate 2019-07-01 00:00:00
2019-07-31 23:45:00
| Room Air Temperature (C)
float64 19.3
35
| Outdoor Air Temperature (C)
float64 11
35
| Outdoor Humidity (%)
float64 0.05
0.97
| Direct Solar Radiation (W/m^2)
float64 0
923
| Wind Speed (m/s)
float64 0
12.6
| Cooling Setpoint (C)
float64 22.5
35
| Heating Setpoint (C)
float64 10
22.5
| HVAC Power Consumption (W)
float64 0
2.1k
|
|---|---|---|---|---|---|---|---|---|
2019-07-01 00:00:00
| 20.133651
| 20.98249
| 0.389024
| 0
| 9.774858
| 30
| 15
| 0
|
2019-07-01 00:15:00
| 20.27766
| 20.677497
| 0.435107
| 0
| 10.148867
| 30
| 15
| 0
|
2019-07-01 00:30:00
| 20.295976
| 20.333753
| 0.500361
| 0
| 10.504075
| 30
| 15
| 0
|
2019-07-01 00:45:00
| 20.262584
| 20.02626
| 0.554248
| 0
| 10.789972
| 30
| 15
| 0
|
2019-07-01 01:00:00
| 20.214945
| 19.803333
| 0.576331
| 0
| 10.853886
| 30
| 15
| 0
|
2019-07-01 01:15:00
| 20.165934
| 19.603333
| 0.588381
| 0
| 10.331113
| 30
| 15
| 0
|
2019-07-01 01:30:00
| 20.11524
| 19.403333
| 0.598598
| 0
| 9.382668
| 30
| 15
| 0
|
2019-07-01 01:45:00
| 20.061329
| 19.203333
| 0.606667
| 0
| 8.313592
| 30
| 15
| 0
|
2019-07-01 02:00:00
| 20.002525
| 19.003333
| 0.612688
| 0
| 7.357193
| 30
| 15
| 0
|
2019-07-01 02:15:00
| 19.937433
| 18.803333
| 0.618133
| 0
| 6.425188
| 30
| 15
| 0
|
2019-07-01 02:30:00
| 19.872237
| 18.603333
| 0.623255
| 0
| 5.48641
| 30
| 15
| 0
|
2019-07-01 02:45:00
| 19.809789
| 18.403333
| 0.627856
| 0
| 4.564329
| 30
| 15
| 0
|
2019-07-01 03:00:00
| 19.748681
| 18.203333
| 0.631187
| 0
| 3.686539
| 30
| 15
| 0
|
2019-07-01 03:15:00
| 19.687496
| 18.003333
| 0.631504
| 0
| 2.880971
| 30
| 15
| 0
|
2019-07-01 03:30:00
| 19.625241
| 17.803333
| 0.630211
| 0
| 2.139477
| 30
| 15
| 0
|
2019-07-01 03:45:00
| 19.561121
| 17.603333
| 0.629538
| 0
| 1.445881
| 30
| 15
| 0
|
2019-07-01 04:00:00
| 19.494426
| 17.403333
| 0.634427
| 0
| 0.810751
| 30
| 15
| 0
|
2019-07-01 04:15:00
| 19.426545
| 17.203333
| 0.653622
| 0
| 0.336988
| 30
| 15
| 0
|
2019-07-01 04:30:00
| 19.362179
| 17.003333
| 0.678614
| 3.439861
| 0.058162
| 30
| 15
| 0
|
2019-07-01 04:45:00
| 19.30825
| 16.803333
| 0.696581
| 23.680924
| 0
| 30
| 15
| 0
|
2019-07-01 05:00:00
| 19.272464
| 16.603333
| 0.695992
| 61.409919
| 0.134305
| 30
| 15
| 0
|
2019-07-01 05:15:00
| 19.257597
| 16.403333
| 0.677237
| 113.213051
| 0.635976
| 30
| 15
| 0
|
2019-07-01 05:30:00
| 19.259932
| 16.203333
| 0.650776
| 182.392726
| 1.273075
| 30
| 15
| 0
|
2019-07-01 05:45:00
| 19.269743
| 16.003333
| 0.627744
| 285.513584
| 1.767904
| 30
| 15
| 0
|
2019-07-01 06:00:00
| 19.294833
| 15.90273
| 0.617836
| 403.355531
| 1.934233
| 30
| 15
| 0
|
2019-07-01 06:15:00
| 19.365659
| 16.2517
| 0.619327
| 507.047662
| 1.979907
| 30
| 15
| 0
|
2019-07-01 06:30:00
| 19.505122
| 16.969307
| 0.622623
| 577.196821
| 2.011093
| 30
| 15
| 0
|
2019-07-01 06:45:00
| 19.705868
| 17.84646
| 0.61788
| 633.935974
| 2.059987
| 30
| 15
| 0
|
2019-07-01 07:00:00
| 19.956918
| 18.709338
| 0.598975
| 684.500857
| 2.148879
| 30
| 15
| 0
|
2019-07-01 07:15:00
| 20.255255
| 19.562254
| 0.575069
| 728.110906
| 2.25841
| 30
| 15
| 0
|
2019-07-01 07:30:00
| 20.563868
| 20.417594
| 0.549823
| 765.584395
| 2.382102
| 30
| 15
| 0
|
2019-07-01 07:45:00
| 20.863633
| 21.266267
| 0.523692
| 801.689289
| 2.521766
| 30
| 15
| 0
|
2019-07-01 08:00:00
| 21.881699
| 22.102733
| 0.49685
| 834.10667
| 2.723597
| 24
| 21
| 3.419666
|
2019-07-01 08:15:00
| 22.851159
| 22.934628
| 0.468857
| 858.433541
| 3.118554
| 24
| 21
| 0
|
2019-07-01 08:30:00
| 23.337139
| 23.76182
| 0.440608
| 872.398041
| 3.595915
| 24
| 21
| 0
|
2019-07-01 08:45:00
| 23.723576
| 24.580738
| 0.413376
| 882.119952
| 3.98041
| 24
| 21
| 0
|
2019-07-01 09:00:00
| 24.086854
| 25.384401
| 0.389678
| 889.922691
| 4.130922
| 24
| 21
| 5.241513
|
2019-07-01 09:15:00
| 24.161209
| 26.150828
| 0.373782
| 896.193597
| 4.091135
| 24
| 21
| 65.572772
|
2019-07-01 09:30:00
| 24.048537
| 26.861273
| 0.362042
| 901.49033
| 3.934786
| 24
| 21
| 110.226616
|
2019-07-01 09:45:00
| 24.047207
| 27.499664
| 0.348833
| 906.487407
| 3.718125
| 24
| 21
| 148.954032
|
2019-07-01 10:00:00
| 24.039163
| 28.065833
| 0.329794
| 910.555302
| 3.455831
| 24
| 21
| 187.814505
|
2019-07-01 10:15:00
| 24.034209
| 28.615833
| 0.306715
| 912.760659
| 3.055397
| 24
| 21
| 224.762612
|
2019-07-01 10:30:00
| 24.030168
| 29.165833
| 0.281261
| 911.114707
| 2.600454
| 24
| 21
| 260.216359
|
2019-07-01 10:45:00
| 24.026137
| 29.715833
| 0.254266
| 902.439057
| 2.22761
| 24
| 21
| 293.314909
|
2019-07-01 11:00:00
| 24.022737
| 30.265833
| 0.222914
| 890.794837
| 2.121398
| 24
| 21
| 323.488173
|
2019-07-01 11:15:00
| 24.019933
| 30.815833
| 0.177873
| 882.000558
| 2.46877
| 24
| 21
| 351.461757
|
2019-07-01 11:30:00
| 24.016345
| 31.365833
| 0.130486
| 879.921616
| 3.034689
| 24
| 21
| 375.26526
|
2019-07-01 11:45:00
| 24.014252
| 31.915833
| 0.097525
| 880.135981
| 3.490422
| 24
| 21
| 396.770828
|
2019-07-01 12:00:00
| 24.014313
| 32.433685
| 0.088792
| 880.622569
| 3.541966
| 24
| 21
| 416.895803
|
2019-07-01 12:15:00
| 24.015939
| 32.803699
| 0.086292
| 880.973972
| 3.211698
| 24
| 21
| 440.5787
|
2019-07-01 12:30:00
| 24.015206
| 33.045237
| 0.083792
| 880.905121
| 2.784663
| 24
| 21
| 464.677598
|
2019-07-01 12:45:00
| 24.013742
| 33.218454
| 0.081292
| 880.593212
| 2.562705
| 24
| 21
| 487.309579
|
2019-07-01 13:00:00
| 24.012181
| 33.376421
| 0.078711
| 879.962336
| 2.750932
| 24
| 21
| 507.941868
|
2019-07-01 13:15:00
| 24.010472
| 33.53047
| 0.075862
| 878.820202
| 3.123864
| 24
| 21
| 526.235025
|
2019-07-01 13:30:00
| 24.008671
| 33.679562
| 0.073083
| 875.933193
| 3.538626
| 24
| 21
| 541.821577
|
2019-07-01 13:45:00
| 24.006855
| 33.825371
| 0.070844
| 867.416898
| 3.926857
| 24
| 21
| 554.501827
|
2019-07-01 14:00:00
| 24.005171
| 33.969037
| 0.069576
| 853.861008
| 4.260977
| 24
| 21
| 564.303451
|
2019-07-01 14:15:00
| 24.003747
| 34.107707
| 0.069289
| 837.191484
| 4.626396
| 24
| 21
| 571.551327
|
2019-07-01 14:30:00
| 24.00244
| 34.23605
| 0.069557
| 818.544965
| 4.949919
| 24
| 21
| 576.521107
|
2019-07-01 14:45:00
| 24.00124
| 34.348708
| 0.069911
| 796.674025
| 5.108502
| 24
| 21
| 579.289296
|
2019-07-01 15:00:00
| 23.99991
| 34.378647
| 0.07
| 773.045443
| 5.039583
| 24
| 21
| 580.009496
|
2019-07-01 15:15:00
| 23.997227
| 34.131861
| 0.07
| 750.037982
| 4.914583
| 24
| 21
| 576.744296
|
2019-07-01 15:30:00
| 23.995352
| 33.733617
| 0.07
| 730.568438
| 4.789583
| 24
| 21
| 569.13537
|
2019-07-01 15:45:00
| 23.99617
| 33.396415
| 0.07
| 717.080799
| 4.664583
| 24
| 21
| 561.670278
|
2019-07-01 16:00:00
| 23.998533
| 33.271803
| 0.0708
| 706.378478
| 4.539583
| 24
| 21
| 557.689205
|
2019-07-01 16:15:00
| 24.000194
| 33.242804
| 0.075003
| 694.07845
| 4.414583
| 24
| 21
| 557.297954
|
2019-07-01 16:30:00
| 24.000656
| 33.249465
| 0.081354
| 674.475227
| 4.289583
| 24
| 21
| 558.376666
|
2019-07-01 16:45:00
| 24.000675
| 33.279528
| 0.08751
| 640.30153
| 4.164583
| 24
| 21
| 559.45636
|
2019-07-01 17:00:00
| 24.001172
| 33.374705
| 0.089944
| 593.232852
| 4.017633
| 24
| 21
| 561.094533
|
2019-07-01 17:15:00
| 24.002006
| 33.667501
| 0.08469
| 537.127581
| 3.779124
| 24
| 21
| 564.268673
|
2019-07-01 17:30:00
| 24.001542
| 33.974335
| 0.077506
| 468.748839
| 3.488603
| 24
| 21
| 567.444119
|
2019-07-01 17:45:00
| 23.99895
| 34.029824
| 0.076361
| 369.849512
| 3.216
| 24
| 21
| 567.336606
|
2019-07-01 18:00:00
| 25.889371
| 33.669677
| 0.087682
| 258.181096
| 3.141001
| 30
| 15
| 18.876898
|
2019-07-01 18:15:00
| 27.531714
| 33.149335
| 0.108762
| 161.421521
| 3.62616
| 30
| 15
| 0
|
2019-07-01 18:30:00
| 27.978701
| 32.58326
| 0.134104
| 98.684282
| 4.388041
| 30
| 15
| 0
|
2019-07-01 18:45:00
| 28.080002
| 31.999901
| 0.158786
| 53.408024
| 4.976374
| 30
| 15
| 0
|
2019-07-01 19:00:00
| 28.065139
| 31.422145
| 0.179483
| 20.762364
| 4.908346
| 30
| 15
| 0
|
2019-07-01 19:15:00
| 27.997327
| 30.846346
| 0.199087
| 3.172149
| 3.960958
| 30
| 15
| 0
|
2019-07-01 19:30:00
| 27.902854
| 30.270365
| 0.217522
| 0
| 2.670162
| 30
| 15
| 0
|
2019-07-01 19:45:00
| 27.799753
| 29.695865
| 0.233241
| 0
| 1.705922
| 30
| 15
| 0
|
2019-07-01 20:00:00
| 27.69413
| 29.124953
| 0.2439
| 0
| 1.533248
| 30
| 15
| 0
|
2019-07-01 20:15:00
| 27.5845
| 28.561668
| 0.246893
| 0
| 1.738196
| 30
| 15
| 0
|
2019-07-01 20:30:00
| 27.472871
| 28.009579
| 0.246209
| 0
| 2.141966
| 30
| 15
| 0
|
2019-07-01 20:45:00
| 27.361983
| 27.4719
| 0.247331
| 0
| 2.73259
| 30
| 15
| 0
|
2019-07-01 21:00:00
| 27.253864
| 26.963503
| 0.254318
| 0
| 3.532443
| 30
| 15
| 0
|
2019-07-01 21:15:00
| 27.152042
| 26.525531
| 0.264412
| 0
| 4.601799
| 30
| 15
| 0
|
2019-07-01 21:30:00
| 27.05784
| 26.142919
| 0.275232
| 0
| 5.763866
| 30
| 15
| 0
|
2019-07-01 21:45:00
| 26.968475
| 25.784729
| 0.285372
| 0
| 6.784443
| 30
| 15
| 0
|
2019-07-01 22:00:00
| 26.880169
| 25.440242
| 0.293247
| 0
| 7.464535
| 30
| 15
| 0
|
2019-07-01 22:15:00
| 26.796363
| 25.15383
| 0.297966
| 0
| 7.814046
| 30
| 15
| 0
|
2019-07-01 22:30:00
| 26.722571
| 24.889528
| 0.3015
| 0
| 7.90715
| 30
| 15
| 0
|
2019-07-01 22:45:00
| 26.653131
| 24.586397
| 0.306359
| 0
| 7.804959
| 30
| 15
| 0
|
2019-07-01 23:00:00
| 26.578328
| 24.17817
| 0.316235
| 0
| 7.452923
| 30
| 15
| 0
|
2019-07-01 23:15:00
| 26.488282
| 23.632643
| 0.335177
| 0
| 6.570635
| 30
| 15
| 0
|
2019-07-01 23:30:00
| 26.381538
| 23.026581
| 0.357379
| 0
| 5.498536
| 30
| 15
| 0
|
2019-07-01 23:45:00
| 26.266473
| 22.456411
| 0.374804
| 0
| 4.743954
| 30
| 15
| 0
|
2019-07-02 00:00:00
| 26.155494
| 21.989963
| 0.382417
| 0
| 4.717083
| 30
| 15
| 0
|
2019-07-02 00:15:00
| 26.053895
| 21.570835
| 0.387417
| 0
| 5.244371
| 30
| 15
| 0
|
2019-07-02 00:30:00
| 25.958479
| 21.172601
| 0.392417
| 0
| 5.961609
| 30
| 15
| 0
|
2019-07-02 00:45:00
| 25.866486
| 20.791688
| 0.397417
| 0
| 6.538641
| 30
| 15
| 0
|
End of preview. Expand
in Data Studio
TTM4HVAC – Evaluation dataset (target-cool-test)
This dataset contains target-building time-series data under cooling-dominated operating conditions (July).
It is intended for evaluation / benchmarking of TTM4HVAC models on a cooling-focused scenario.
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)
Usage
from datasets import load_dataset
ds = load_dataset("gft/ttm4hvac-target-heat-test")
df = ds["test"].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
- 17
Size of downloaded dataset files:
200 kB
Size of the auto-converted Parquet files:
200 kB
Number of rows:
2,976
Models trained or fine-tuned on gft/ttm4hvac-target-cool-test