Add dataset card

README.md

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+---
+license: mit
+task_categories:
+  - time-series-forecasting
+language:
+  - en
+tags:
+  - nilm
+  - energy-disaggregation
+  - uk-dale
+  - high-frequency
+size_categories:
+  - 1K<n<10K
+configs:
+  - config_name: default
+    data_files:
+      - split: train
+        path: train/*
+      - split: val
+        path: val/*
+      - split: benchmark
+        path: benchmark/*
+---
+
+# NILMbench processed UK-DALE splits
+
+Pre-processed 16 kHz voltage/current frames and per-category active-power
+labels from the UK-DALE 2015 release, packaged for the NILMbench benchmark
+(House 1 → House 2 cross-household evaluation).
+
+## Layout
+
+```
+train/      10,000 sparse class-balanced 6-second frames from House 1
+val/         1,000 sparse class-balanced 6-second frames from House 1
+benchmark/   2,000 sparse class-balanced 6-second frames from House 2
+```
+
+Each split contains:
+
+| File                     | Shape          | Description                                |
+| ------------------------ | -------------- | ------------------------------------------ |
+| `x_vi_6s.npy`            | `(N, 2, 96000)` float16 | 16 kHz V/I waveform per frame (FLAC-normalised, range `[-1, 1]`) |
+| `labels_and_index.npz`   | dict            | per-category power label, on/off label, aggregate context, timestamp, source window id |
+
+`labels_and_index.npz` contains:
+
+* `y_power`       `(N, 7)` float32 — active power in watts per scored category
+* `y_state`       `(N, 7)` bool — on/off label per category
+* `x_agg`         `(N, 11)` float32 — aggregate-power context (±5 frames, ±30 s)
+* `timestamp`     `(N,)` int64 — Unix seconds of frame centre
+* `sample_idx`    `(N,)` int16 — 0..599 index inside the source window
+* `window_id`     `(N,)` str  — UK-DALE window identifier
+* `class_names`   `(7,)` str  — ordered category names
+
+## Recovering engineering units
+
+The V/I waveforms are stored in FLAC-normalised form (range `[-1, 1]`). To
+get volts and amperes, multiply by the UK-DALE House-2 calibration constants:
+
+```python
+V_FACTOR = (2 ** 31) * 1.88296904357e-7   # ≈ 404.4
+I_FACTOR = (2 ** 31) * 4.77518864497e-8   # ≈ 102.5
+```
+
+## Usage
+
+```python
+import numpy as np
+from huggingface_hub import snapshot_download
+
+root = snapshot_download(repo_id="Pybunny/nilmbench-ukdale", repo_type="dataset")
+x = np.load(f"{root}/train/x_vi_6s.npy", mmap_mode="r")
+labels = np.load(f"{root}/train/labels_and_index.npz", allow_pickle=True)
+print(x.shape, labels["y_power"].shape, labels["class_names"])
+```
+
+## Citation
+
+NILMbench paper (2026), and the original UK-DALE dataset by Kelly &
+Knottenbelt (2015).
+
+## License
+
+MIT for the processed splits and metadata. The underlying UK-DALE recordings
+are subject to their original license (CC-BY 4.0).