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Cited by 330Smart Grid Energy ManagementEnergy Load and Power ForecastingIoT-based Smart Home Systems

Transfer Learning for Non-Intrusive Load Monitoring

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D'Incecco, Michele, Squartini, Stefano, Zhong, Mingjun·2020·IEEE Transactions on Smart Grid
DOI 10.1109/tsg.2019.2938068
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Abstract

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.

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  • In real-world AIoT systems, there are always many related tasks involved, e.g., object detection and tracking, and semantic segmentation in video surveillance. Therefore, finding transfer learning dependencies across these tasks and leveraging such prior knowledge to learn better models are of practical value for AIoT [199], [200], [201], [202].

    “In real-world AIoT systems, there are always many related tasks involved, e.g., object detection and tracking, and semantic segmentation in video surveillance. Therefore, finding the transfer learning dependencies across these tasks and leveraging such prior knowledge to learn better models, are of practical value for AIoT [199], [200], [201], [202].”

    From Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things (2021) · cites this paper as [200]

  • Transfer learning and deep reinforcement learning methods are proposed for load monitoring and electric vehicle charging scheduling.

    “For load monitoring and electric vehicle charging scheduling, transfer learning [200] and deep reinforcement learning [215] methods are proposed.”

    From Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things (2021) · cites this paper as [200]

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Authors
D'Incecco, M., Squartini, S., & Zhong, M.
Affiliations
  • Marche Polytechnic University (IT)
  • University of Lincoln (GB)
ORCID
Venue
IEEE Transactions on Smart Grid
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2020-03
Volume
11
Issue
2
Pages
1419-1429
ISSN
  • 1949-3053 (ISSN-L)
  • 1949-3061
DOI
10.1109/tsg.2019.2938068
OpenAlex
W2971067036
Cited by
330
References
47
Citations by year
2026: 29 · 2025: 62 · 2024: 43 · 2023: 67 · 2022: 57 · 2021: 46 · 2020: 23 · 2019: 3
FWCI
11.58
Type
journal article
Language
en
Open access
Licence
Locations
  • IEEE Transactions on Smart Grid (publishedVersion)
  • Lincoln Repository (University of Lincoln) (acceptedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • Figshare (submittedVersion, cc-by)
  • Università Politecnica delle Marche (Università Politecnica delle Marche) (submittedVersion, other-oa)
  • arXiv (Cornell University)
Topics
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Energy Load and Power Forecasting
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › IoT-based Smart Home Systems
Keywords
non-intrusive load monitoring, transfer learning, appliance identification, cross-domain transfer, fine-tuning, latent features, household electricity consumption, non-identifiability

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