  Non-intrusive load monitoring (NILM) is a technique to recover source
appliances from only the recorded mains in a household. NILM is unidentifiable
and thus a challenge problem because the inferred power value of an appliance
given only the mains could not be unique. To mitigate the unidentifiable
problem, various methods incorporating domain knowledge into NILM have been
proposed and shown effective experimentally. Recently, among these methods,
deep neural networks are shown performing best. Arguably, the recently proposed
sequence-to-point (seq2point) learning is promising for NILM. However, the
results were only carried out on the same data domain. It is not clear if the
method could be generalised or transferred to different domains, e.g., the test
data were drawn from a different country comparing to the training data. We
address this issue in the paper, and two transfer learning schemes are
proposed, i.e., appliance transfer learning (ATL) and cross-domain transfer
learning (CTL). For ATL, our results show that the latent features learnt by a
`complex' appliance, e.g., washing machine, can be transferred to a `simple'
appliance, e.g., kettle. For CTL, our conclusion is that the seq2point learning
is transferable. Precisely, when the training and test data are in a similar
domain, seq2point learning can be directly applied to the test data without
fine tuning; when the training and test data are in different domains,
seq2point learning needs fine tuning before applying to the test data.
Interestingly, we show that only the fully connected layers need fine tuning
for transfer learning. Source code can be found at
https://github.com/MingjunZhong/transferNILM.
