Pybunny/nilmbench-ukdale

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:

V_FACTOR = (2 ** 31) * 1.88296904357e-7   # ≈ 404.4
I_FACTOR = (2 ** 31) * 4.77518864497e-8   # ≈ 102.5

Usage

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).