ai-iot/Forecasting-W-LSTMix
Updated
Datasets:
Dataset Viewer
mels_S
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⌀ | Timestamp
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|---|---|---|---|---|---|
1.2 | 0.2 | 7.5 | 37.400002 | 19.5 |
2018-01-01 01:00:00
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1.3 | 0.2 | 6.8 | 37.5 | 19.889999 |
2018-01-01 01:15:00
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1.1 | 0.2 | 7.4 | 38 | 19.299999 |
2018-01-01 01:30:00
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1.2 | 0.2 | 7.7 | 37.200001 | 18.889999 |
2018-01-01 01:45:00
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1.1 | 0.2 | 7.3 | 37.400002 | 24.700001 |
2018-01-01 02:00:00
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1.15 | 0.2 | 7.4 | 37.200001 | 24.9 |
2018-01-01 02:15:00
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1.2 | 0.2 | 7.5 | 38.200001 | 21.4 |
2018-01-01 02:30:00
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1 | 0.2 | 8 | 38.200001 | 20.1 |
2018-01-01 02:45:00
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1.1 | 0.2 | 6.9 | 37.400002 | 18.700001 |
2018-01-01 03:00:00
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1.3 | 0.1 | 8.3 | 37.200001 | 18.889999 |
2018-01-01 03:15:00
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1.2 | 0.15 | 7.9 | 37.900002 | 19.200001 |
2018-01-01 03:30:00
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1.1 | 0.2 | 7.5 | 37.700001 | 18.889999 |
2018-01-01 03:45:00
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1.4 | 0.2 | 7.2 | 37.200001 | 23.299999 |
2018-01-01 04:00:00
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1.2 | 0.1 | 7.9 | 36.900002 | 23.9 |
2018-01-01 04:15:00
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1.4 | 0.2 | 6.7 | 35.400002 | 20.700001 |
2018-01-01 04:30:00
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1.3 | 0.2 | 7 | 37.200001 | 18.5 |
2018-01-01 04:45:00
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1.2 | 0.2 | 6.95 | 36.200001 | 18.6 |
2018-01-01 05:00:00
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1.1 | 0.2 | 6.9 | 37.5 | 19.200001 |
2018-01-01 05:15:00
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1.2 | 0.2 | 6.3 | 37.790001 | 19 |
2018-01-01 05:30:00
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1.166667 | 0.2 | 6.433333 | 37.599998 | 19.200001 |
2018-01-01 05:45:00
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1.133333 | 0.2 | 6.566667 | 37.599998 | 19.5 |
2018-01-01 06:00:00
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1.1 | 0.2 | 6.7 | 38.200001 | 24.700001 |
2018-01-01 06:15:00
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1.1 | 0.2 | 6.7 | 38.290001 | 25.9 |
2018-01-01 06:30:00
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1.1 | 0.2 | 6.7 | 38.290001 | 22.6 |
2018-01-01 06:45:00
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1.1 | 0.2 | 6.7 | 36.700001 | 20.1 |
2018-01-01 07:00:00
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1.2 | 0.1 | 7.2 | 37.200001 | 20.700001 |
2018-01-01 07:15:00
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1.3 | 0.2 | 7.4 | 38.099998 | 20.389999 |
2018-01-01 07:30:00
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1.2 | 0.2 | 6.9 | 37.700001 | 20.1 |
2018-01-01 07:45:00
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1.1 | 0.2 | 7.5 | 38.5 | 19.7 |
2018-01-01 08:00:00
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1.133333 | 0.2 | 7.266667 | 38.29 | 23 |
2018-01-01 08:15:00
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1.166667 | 0.2 | 7.033333 | 36.6 | 24.3 |
2018-01-01 08:30:00
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1.2 | 0.2 | 6.8 | 36.400002 | 20.700001 |
2018-01-01 08:45:00
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1.2 | 0.2 | 6.8 | 36.7 | 18.89 |
2018-01-01 09:00:00
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1.25 | 0.2 | 7.15 | 37 | 18.389999 |
2018-01-01 09:15:00
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1.3 | 0.2 | 7.5 | 37.6 | 20.1 |
2018-01-01 09:30:00
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1.4 | 0.2 | 7.3 | 37.900002 | 19.299999 |
2018-01-01 09:45:00
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1.2 | 0.2 | 6.7 | 37.400002 | 19.6 |
2018-01-01 10:00:00
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1.1 | 0.15 | 6.85 | 36.7 | 18.7 |
2018-01-01 10:15:00
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1 | 0.1 | 7 | 35.790001 | 17.700001 |
2018-01-01 10:30:00
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1.1 | 0.2 | 7.3 | 36.6 | 18.6 |
2018-01-01 10:45:00
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0.8 | 0.2 | 7.3 | 37 | 24.700001 |
2018-01-01 11:00:00
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0.95 | 0.2 | 6.95 | 36.700001 | 23.700001 |
2018-01-01 11:15:00
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1.1 | 0.2 | 6.6 | 66.400002 | 20 |
2018-01-01 11:30:00
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1.2 | 0.2 | 6.9 | 37.900002 | 20.1 |
2018-01-01 11:45:00
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1.2 | 0.2 | 7.6 | 36.4 | 17.8 |
2018-01-01 12:00:00
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1.2 | 0.2 | 6.7 | 35.700001 | 18.299999 |
2018-01-01 12:15:00
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1.1 | 0.2 | 7.5 | 37 | 18.200001 |
2018-01-01 12:30:00
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1.3 | 0.2 | 7.4 | 37.4 | 18.39 |
2018-01-01 12:45:00
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1.2 | 0.2 | 7.45 | 36.599998 | 18.389999 |
2018-01-01 13:00:00
|
1.1 | 0.2 | 7.5 | 35.79 | 18.39 |
2018-01-01 13:15:00
|
1 | 0.2 | 7.4 | 36.1 | 19.39 |
2018-01-01 13:30:00
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1.1 | 0.2 | 7.2 | 36.9 | 18.8 |
2018-01-01 13:45:00
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1.2 | 0.2 | 7.1 | 36.790001 | 19.389999 |
2018-01-01 14:00:00
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1.2 | 0.2 | 7 | 37 | 19.2 |
2018-01-01 14:15:00
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1.3 | 0.3 | 8.4 | 36.099998 | 18.1 |
2018-01-01 14:30:00
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1.15 | 0.2 | 7.2 | 35.5 | 18 |
2018-01-01 14:45:00
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1 | 0.3 | 7.5 | 35.79 | 23 |
2018-01-01 15:00:00
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1.2 | 0.3 | 8 | 36.5 | 23.700001 |
2018-01-01 15:15:00
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1.15 | 0.3 | 7.55 | 36.5 | 23.799999 |
2018-01-01 15:30:00
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1.1 | 0.3 | 7.1 | 36.700001 | 20.700001 |
2018-01-01 15:45:00
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1 | 0.3 | 7.7 | 36.290001 | 19.5 |
2018-01-01 16:00:00
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1.05 | 0.3 | 7.65 | 36.79 | 18.6 |
2018-01-01 16:15:00
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1.1 | 0.3 | 7.6 | 37.200001 | 18.889999 |
2018-01-01 16:30:00
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1.15 | 0.3 | 7.55 | 37.200001 | 19.299999 |
2018-01-01 16:45:00
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1.2 | 0.3 | 7.5 | 35.900002 | 23 |
2018-01-01 17:00:00
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1.2 | 0.3 | 8 | 36 | 23.1 |
2018-01-01 17:15:00
|
1.2 | 0.3 | 7.4 | 36.6 | 19.2 |
2018-01-01 17:30:00
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1.2 | 0.3 | 6.8 | 36.700001 | 18.700001 |
2018-01-01 17:45:00
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1.3 | 0.3 | 7.1 | 37.099998 | 18.889999 |
2018-01-01 18:00:00
|
1.4 | 0.2 | 7.9 | 37.099998 | 19.299999 |
2018-01-01 18:15:00
|
1.2 | 0.3 | 6.9 | 37 | 19.2 |
2018-01-01 18:30:00
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1.233333 | 0.6 | 9.533333 | 37.9 | 18.1 |
2018-01-01 18:45:00
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1.266667 | 0.9 | 12.166667 | 36.599998 | 23.700001 |
2018-01-01 19:00:00
|
1.3 | 1.2 | 14.8 | 37.400002 | 24 |
2018-01-01 19:15:00
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1.4 | 1.15 | 15 | 36.1 | 18.39 |
2018-01-01 19:30:00
|
1.5 | 1.1 | 15.2 | 36.29 | 18.7 |
2018-01-01 19:45:00
|
1.3 | 0.1 | 14.2 | 36.5 | 18.8 |
2018-01-01 20:00:00
|
1.3 | 0.64 | 14.16 | 36.7 | 19.2 |
2018-01-01 20:15:00
|
1.3 | 1.18 | 14.12 | 37.099998 | 19.389999 |
2018-01-01 20:30:00
|
1.3 | 1.72 | 14.08 | 37.4 | 20.39 |
2018-01-01 20:45:00
|
1.3 | 2.26 | 14.04 | 38.290001 | 20 |
2018-01-01 21:00:00
|
1.3 | 2.8 | 14 | 37.2 | 25.3 |
2018-01-01 21:15:00
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1.4 | 2.6 | 15.1 | 37.2 | 25.6 |
2018-01-01 21:30:00
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1.45 | 2.1 | 14.55 | 35.599998 | 20.6 |
2018-01-01 21:45:00
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1.5 | 1.6 | 14 | 36.7 | 17.89 |
2018-01-01 22:00:00
|
1.2 | 1.3 | 14.2 | 37.5 | 18.6 |
2018-01-01 22:15:00
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1.2 | 1.4 | 14.2 | 38.790001 | 21.299999 |
2018-01-01 22:30:00
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1.3 | 1.4 | 14.2 | 38.5 | 21.299999 |
2018-01-01 22:45:00
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1.4 | 1.4 | 14.2 | 38.2 | 20.39 |
2018-01-01 23:00:00
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1.3 | 1.4 | 13.9 | 37.1 | 18.5 |
2018-01-01 23:15:00
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1.2 | 1.4 | 13.6 | 39.400002 | 25.200001 |
2018-01-01 23:30:00
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1.3 | 1.4 | 14.2 | 38.900002 | 25.200001 |
2018-01-01 23:45:00
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1.4 | 1.4 | 14.8 | 38.599998 | 21.5 |
2018-01-02 00:00:00
|
1.3 | 1.45 | 14.35 | 37.6 | 44.3 |
2018-01-02 00:15:00
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1.2 | 1.5 | 13.9 | 38.2 | 19.6 |
2018-01-02 00:30:00
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1.2 | 1.7 | 13.95 | 38.5 | 20.200001 |
2018-01-02 00:45:00
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1.2 | 1.9 | 14 | 38.400002 | 19.799999 |
2018-01-02 01:00:00
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1.2 | 1.5 | 13.3 | 37.790001 | 19.6 |
2018-01-02 01:15:00
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1.2 | 0.1 | 14 | 38.400002 | 19.799999 |
2018-01-02 01:30:00
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0.7 | 0.2 | 13.9 | 38.599998 | 25.5 |
2018-01-02 01:45:00
|
End of preview. Expand
in Data Studio
EnergyBench Dataset
Overview
A open-source large-scale energy meter dataset designed to support a variety of energy analytics applications, including load profile analysis, and load forecasting. It compiles over 60 detailed electricity consumption datasets encompassing approximately 78,037 real buildings representing the global building stock, offering insights into the temporal and spatial variations in energy consumption. The buildings are classified into two categories: Commercial, which includes 2,916 buildings, and Residential, which includes 75,121 buildings, with a total of 65,000 building-days of time-series data. These buildings span diverse geographies, climatic zones, and operational profiles, providing a broad spectrum of usage patterns. Additional to the real buildings, it also includes synthetic and simulated buildings electricity consumption data.
All processed datasets contain hourly data, with some offering additional 15-minute and 30-minute resolution. To ensure efficient storage, we saved the datasets in Parquet format rather than CSV. For datasets involving a large number of buildings, we partitioned the data into multiple Parquet files.
Metadata of each datasets are provided in metadata.
Table 1: Summary of EnergBench dataset (Real Buildings)
| Category | # Datasets | # Buildings | # Days | # Hourly Obs |
|---|---|---|---|---|
| Commercial | 20 | 2,916 | 16,706 | 62M |
| Residential | 47 | 75,121 | 43,852 | 1.2B |
Table 2: Summary of EnergBench dataset (Synthetic and Simulated Buildings)
| Category | # Datasets | # Buildings | # Days | # Hourly Obs |
|---|---|---|---|---|
| Commercial | 1 | 207,559 | 365 | ~1.7B |
| Residential | 4 | 31M | 966 | ~718.7B |
Dataset Details
Below tables shows the summary of the real buildings
Table 3: Details of real commercial buildings
| Dataset | Location | # Buildings | Year | # Days | # Hourly Obs | Unit | Resolution | Type |
|---|---|---|---|---|---|---|---|---|
| IBlend | India | 9 | 2013-17 | 1,604 | 296,357 | kW | 15min, 1H | Mains |
| CLEMD | Malaysia | 1 | 2023-23 | 34 | 706 | kW | 15min, 1H | Mains |
| CU-BEMS | Thailand | 1 | 2018-19 | 548 | 1,504,058 | kW | 15min, 1H | Circuits |
| IPC-Commercial | China | 3 | 2016-21 | 2,191 | 132,519 | kW | 1H | Mains |
| EWELD | Southern China | 386 | 2016-22 | 2,260 | 13,730,258 | kWh | 15min,1H | Mains |
| SKC | South Korea | 10 | 2019 | 213 | 50,708 | kW | 15min, 1H | Mains |
| IOT | Sharjah | 1 | 2024 | 171 | 2,163 | kWh | 1H | Circuits |
| SEWA | UAE | 1 | 2020-21 | 730 | 17,489 | kW | 1H | Mains |
| PSS | South Africa | 53 | 2022-23 | 364 | 454,774 | kWh | 30min, 1H | Mains |
| Enernoc | USA | 100 | 2012-13 | 366 | 877,728 | kWh | 15min, 1H | Mains |
| DGS | USA | 322 | 2016-18 | 1,064 | 5,333,851 | NA | 15min, 1H | Mains |
| Berkely | USA | 1 | 2018-21 | 1,096 | 126,159 | kW | 15min, 1H | Circuits |
| HB | Phoenix, USA | 1 | 2023 | 364 | 87,600 | kWh | 1H | Circuits |
| BDG-2 | USA | 1,578 | 2016-17 | 730 | 25,212,579 | kWh | 1H | Mains |
| ULE | Cambridge | 1 | 2023-24 | 365 | 8,783 | kWh | 1H | Mains |
| ECRG-Commercial | Poland | 10 | 2,017 | 7 | 1,690 | kW | 15min, 1H | Mains |
| RKP | Portugal | 3 | 2019 | 98 | 1,319 | kW | 15min, 1H | Mains |
| UCIE | Portugal | 370 | 2011-15 | 1,461 | 12,974,050 | kWh | 15min, 1H | Mains |
| NEST-Commercial | Switzerland | 1 | 2019-23 | 1,460 | 34,798 | kW | 15min, 1H | Mains |
| UNICON | Australia | 64 | 2018-22 | 1,580 | 2,066,301 | kW | 15min, 1H | Mains |
| Total | 2,916 | 16,706 | 62M |
Table 4: Details of real residential buildings
| Dataset | Location | # Buildings | Year | # Days | # Hourly Obs | Unit | Resolution | Type |
|---|---|---|---|---|---|---|---|---|
| Prayas | India | 116 | 2018-20 | 911 | 1,536,409 | kWh | 15min, 1H | Mains |
| CEEW | India | 84 | 2019-21 | 914 | 923,897 | kWh | 15min, 1H | Mains |
| RSL | Sri Lanka | 2,903 | 2023-24 | 449 | 11,372,970 | kW | 15min, 1H | Mains |
| IPC-Residential | China | 1 | 2016-21 | 2,191 | 45,471 | kW | 1H | Mains |
| SFAC | China | 1 | 2021-22 | 366 | 115,857 | kWh | 15min, 1H | Circuits |
| ENERTALK | South Korea | 22 | 2016-17 | 242 | 39,923 | kW | 15min, 1H | Mains |
| DCB | Japan | 1 | 2002-22 | 7,457 | 187,008 | kW | 1H | Mains |
| SRSA | South Africa | 1 | 2016-18 | 662 | 13,966 | kWh | 1H | Mains |
| MIHEC | South Africa | 1 | 2019-21 | 1,013 | 19,358 | kWh | 1H | Mains |
| AMPD | Canada | 1 | 2012-14 | 730 | 17,520 | kW | 15min, 1H | Mains |
| RHC | Canada | 1 | 2012-14 | 730 | 17,520 | kWh | 15min, 1H | Mains |
| HUE | British Columbia | 28 | 2012-20 | 2,910 | 610,949 | kWh | 1H | Mains |
| flEECe | USA | 6 | 2017-18 | 259 | 74,492 | kWh | 1H | Mains |
| MFRED | USA | 26 | 2019-20 | 365 | 227,622 | kW | 15min, 1H | Mains |
| SMART-Star | USA | 114 | 2014-16 | 867 | 958,998 | kW | 15min, 1H | Mains |
| Honda SMART Home | USA | 1 | 2020 | 365 | 8,760 | kW | 15min, 1H | Mains |
| PES | USA, Europe | 10 | 2011-14 | 971 | 13,508 | kWh | 15min, 1H | Mains |
| ECWM | Mexico | 1 | 2022-24 | 426 | 10,184 | kW | 1H | Mains |
| WED | Mexico | 5 | 2022-23 | 378 | 45,096 | kWh | 15min, 1H | Mains |
| REEDD | Costa Rica | 46 | 2018-19 | 384 | 12,058 | kW | 15min, 1H | Circuits |
| REFIT | UK | 20 | 2013-15 | 661 | 231,970 | kW | 15min, 1H | Circuits |
| METER | UK | 311 | 2016-19 | 1,065 | 8,928 | kW | 30min, 1H | Mains |
| SAVE | UK | 4,691 | 2016-18 | 904 | 59,918,911 | kWh | 15min, 1H | Mains |
| HES | UK | 16 | 2010-11 | 455 | 115,824 | kWh | 1H | Mains |
| NDB | UK | 1 | 2022 | 229 | 3,282 | kW | 15min, 1H | Circuits |
| LCL | UK | 5,561 | 2011-14 | 828 | 83,925,571 | kWh | 30min, 1H | Mains |
| UKST | Great Britain | 14,319 | 2008-10 | 996 | 207,579,283 | kWh | 30min, 1H | Mains |
| IRH | Ireland | 20 | 2020-21 | 366 | 174,398 | kW | 15min, 1H | Mains |
| DESM | France | 3 | 2020-21 | 365 | 24,018 | kW | 15min, 1H | Mains |
| IHEPC | Sceaux | 1 | 2006-10 | 1,441 | 34,168 | kW | 15min, 1H | Mains |
| NEEA | Portland | 410 | 2018-20 | 855 | 2,922,289 | kW | 15min, 1H | Mains |
| GoiEner | Spain | 25,559 | 2014-22 | 2,775 | 632,313,933 | kWh | 1H | Mains |
| Plegma | Greece | 13 | 2022-23 | 446 | 83,452 | kW | 15min, 1H | Mains |
| DEDDIAG | Germany | 15 | 2016-20 | 1582 | 732,597 | kW | 15min, 1H | Circuits |
| HSG | Germany | 11 | 2014-19 | 1602 | 220,775 | kWh | 15min, 1H | Mains |
| SFHG | Germany | 38 | 2019-20 | 730 | 769,247 | kW | 1H | Mains |
| GREEND | Italy, Austria | 7 | 2013-15 | 570 | 489,644 | kWh | 15min, 1H | Circuits |
| iFlex | Norway | 4,429 | 2020-21 | 445 | 13,515,600 | kWh | 1H | Mains |
| Norwegian | Norway | 1,136 | 2020-22 | 546 | 14,913,408 | kWh | 1H | Mains |
| LEC | Portugal | 172 | 2022-23 | 485 | 1,507,699 | kWh | 15min, 1H | Mains |
| ECCC | Portugal | 51 | 2019-20 | 365 | 447,984 | kW | 15min, 1H | Mains |
| ECRG-Residential | Poland | 15 | 2017 | 7 | 2,535 | kW | 15min, 1H | Mains |
| NESEMP | Scotland | 215 | 2010-12 | 720 | 1,575,682 | kW | 15min, 1H | Mains |
| NEST-Residential | Switzerland | 2 | 2019-23 | 1,460 | 34715 | kW | 15min, 1H | Mains |
| DTH | Slovak Republic | 1,000 | 2016-17 | 395 | 9,453,051 | kWh | 15min, 1H | Mains |
| SGSC | Australia | 13,735 | 2011-14 | 880 | 172,277,213 | kWh | 30min, 1H | Mains |
| BTS | Australia | 1 | 2021 | 89 | 443,208 | kW | 15min, 1H | Circuits |
| Total | 75,121 | 43,852 | 1.2B |
License
EnergyBench dataset is available for download with permissive CC-4.0 license.
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Size of downloaded dataset files:
166 GB
Size of the auto-converted Parquet files:
166 GB
Number of rows:
43,812,938
Models trained or fine-tuned on ai-iot/EnergyBench