metadata
license: mit
tags:
- nilm
- energy-disaggregation
- uk-dale
- pytorch
library_name: nilmbench
datasets:
- Pybunny/nilmbench-ukdale
language:
- en
FaustineCNN — NILMbench baseline
FaustineCNN trained on UK-DALE House 1 (sparse 6-second 16 kHz V/I frames) and
evaluated on the dense House 2 set of 60,000 frames. This is the best-scoring
baseline from the NILMbench paper (MJ_{20W} = 0.504 raw,
0.559 with the recall-constrained cutoffs in cutoffs.json).
Files
| File | Purpose |
|---|---|
faustine_best.pt |
PyTorch state-dict, loadable with nilmbench.models.FaustineCNN. |
cutoffs.json |
Recall-constrained per-class cutoffs c_k (calibrated on House 1). |
classes.json |
Ordered category names (length 7). |
Quick start
import torch, json
from huggingface_hub import hf_hub_download
from nilmbench.models import FaustineCNN
ckpt = hf_hub_download("Pybunny/nilmbench-faustine", "faustine_best.pt")
classes = json.load(open(hf_hub_download("Pybunny/nilmbench-faustine", "classes.json")))
model = FaustineCNN(n_categories=len(classes))
model.load_state_dict(torch.load(ckpt, map_location="cpu"))
model.eval()
Citation
NILMbench paper (2026). See https://github.com/Saharmgh/NILMbench for the full code and reproducible-figure scripts.
License
MIT.