Transfer Learning for Non-Intrusive Load Monitoring

Michele D'Incecco, Stefano Squartini, Mingjun Zhong

Research output: Contribution to journalArticlepeer-review

208 Citations (Scopus)


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
Original languageEnglish
Article number8818314
Pages (from-to)1419-1429
Number of pages11
JournalIEEE Transactions on Smart Grid
Issue number2
Early online date28 Aug 2019
Publication statusPublished - Mar 2020


  • NILM
  • non-intrusive load monitoring
  • energy disaggregation
  • deep neural networks
  • transfer learning
  • sequence-to-point learning


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