Closed-Loop Learning for Accuracy Improvement of Non-Intrusive Load Monitoring in Smart Homes

Guohong Liu, Liheng Lv, Cong Wang, Xiaomeng Li, Hui Wan, Yanjun Li, Zhe Chen

Research output: Contribution to journalJournal articleResearchpeer-review


Non-intrusive load monitoring (NILM) is a technique that estimates the powers of all loads within a smart home, using only the aggregated power at the main power bus. Up to today, some NILM methods have been well developed. However, their estimation accuracy can be further improved, especially when dealing with the loads that own more than two operating states. In this paper, we propose a closed-loop learning method that explores feedback mechanism to reduce the estimation error of NILM. Specifically, the closed-loop learning consists of a feedforward network and a feedback network. The former aims to transform the aggregated power into load powers, as accurately as possible. The latter balances the operating states and exploits the temporal-spatial correlation of different loads, generating a feedback loss that helps improve the former’s accuracy. The proposed method is evaluated on UK-DALE and REDD datasets, and is compared with the state-of-the-art methods. Results show that the proposed method indeed improves the accuracy of NILM and can be deployed for practical applications.
Original languageEnglish
JournalIEEE Transactions on Instrumentation and Measurement
Pages (from-to)1-1
Number of pages1
Publication statusPublished - 2023


  • Correlation
  • Feature extraction
  • Hidden Markov models
  • Learning systems
  • Load monitoring
  • Non-intrusive load monitoring
  • Smart homes
  • Washing machines
  • accuracy improvement
  • closed-loop learning
  • deep neural network
  • smart home


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