Appliance Signature Space with One For All NILM

NNon-intrusive load monitoring (NILM) estimates the power drawn by individual appliances from a single household meter. Deep NILM models are usually trained one per appliance, so a smart meter that tracks many appliances must store many networks. We study one-for-all NILM (OFAN), in which a single transformer disaggregates whichever appliance it is conditioned on through a token appended to its input sequence, taken either from a learned embedding or from an encoder applied to a representative activation waveform. We add this conditioning to two models: BERT4NILM and NILMformer, and evaluate it on UK-DALE and REFIT over five seeds with paired confidence intervals. For five appliances, one conditioned BERT4NILM model has 1.9M parameters compared to 9.7M for five separate models, and each additional appliance adds a single 256-dimensional embedding. The cost of sharing one model depends on the backbone and the dataset: on UK-DALE, pooled mean absolute error (MAE) rises by 12.8% for NILMformer and by 24.6% for BERT4NILM, while on the REFIT datasets, a single BERT4NILM model lowers MAE by 5.6%. Conditioning on activation waveforms is less accurate than learned embeddings on UK-DALE; however, it transfers what it has learned: applied to the other dataset without retraining and conditioned on the appliance signature it learned in training, a model reaches a mean F1 of $0.43$ over 101 sub-metered channels, above a random detector on every channel and above an always-on detector on 88 of them, and $0.20$ F1 higher than the same model conditioned on a query built in the target home (95% CI $[0.17,0.23]$); appliance types absent from training are not detected. Finally, we read the learned embedding table as a proxy for Hart's appliance signature space and find structure in it that only training can explain: in all four model–dataset pairs, the kettle and microwave, the two appliances with similar short high-power cycles, form the closest pair, and in three of them, they trade accuracy under joint training.