| PV-Augmented NILM Dataset (Hugging Face Dataset Card Release) |
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| Directory layout |
| - REDD_house1, REDD_house2, REDD_house3 |
| - UKDALE_house1, UKDALE_house2 |
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| Files per house |
| - train.csv, test.csv, full.csv |
| - train.pkl, test.pkl, full.pkl |
| - train.h5, test.h5, full.h5 (NILMTK-compatible) |
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| Data formats |
| - CSV: first column is the UTC timestamp index (e.g., 2011-04-19 00:00:00+00:00). |
| - PKL: pickled pandas DataFrame with the same columns and index as the CSV files. |
| - H5: NILMTK-compatible dataset built from the full set. Mains are stored as |
| aggregate_power_with_injection (active) and aggregate_reactive (reactive). |
| Appliances are stored per-column; micro_inverter is exported as device model |
| solar_pv. The same export logic is applied to train.h5 and test.h5. |
| Columns ending with _state or the helper columns below are excluded from H5. |
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| Common columns (all houses, CSV/PKL) |
| - aggregate_power |
| - aggregate_power_with_injection |
| - aggregate_reactive |
| - hour_sin |
| - hour_cos |
| - power_factor |
| - micro_inverter |
| - micro_inv (PV on/off indicator) |
| - micro_inverter_normalized |
| - GHI |
| - DNI |
| - DHI |
| - Wind Speed |
| - Temperature |
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| Appliance columns by dataset (CSV/PKL) |
| - REDD_house1 / REDD_house3 |
| - microwave, microwave_state, microwave_reactive |
| - fridge, fridge_state, fridge_reactive |
| - dish washer, dish washer_state, dish washer_reactive |
| - washing machine, washing machine_state, washing machine_reactive |
| - REDD_house2 |
| - microwave, microwave_state, microwave_reactive |
| - fridge, fridge_state, fridge_reactive |
| - dish washer, dish washer_state, dish washer_reactive |
| - UKDALE_house1 / UKDALE_house2 |
| - kettle, kettle_state, kettle_reactive |
| - microwave, microwave_state, microwave_reactive |
| - fridge, fridge_state, fridge_reactive |
| - dish washer, dish washer_state, dish washer_reactive |
| - washing machine, washing machine_state, washing machine_reactive |
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| Usage examples |
| - CSV |
| - import pandas as pd |
| - df = pd.read_csv('full.csv', index_col=0, parse_dates=True) |
| - PKL |
| - import pandas as pd |
| - df = pd.read_pickle('full.pkl') |
| - H5 (NILMTK) |
| - from nilmtk import DataSet |
| - dataset = DataSet('full.h5') |
| - mains = dataset.buildings[1].elec.mains() |
| - meters = dataset.buildings[1].elec.submeters() |
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