Can non-intrusive load monitoring be used for identifying an appliance's anomalous behaviour?

Haroon Rashid, Pushpendra Singh, Vladimir Stankovic, Lina Stankovic

Research output: Contribution to journalArticle

22 Citations (Scopus)
33 Downloads (Pure)

Abstract

Identification of faulty appliance behaviour in real time can signal energy wastage and the need for appliance servicing or replacement leading to energy savings. The problem of appliance fault or anomaly detection has been tackled vastly in relation to submetering, which is not scalable since it requires separate meters for each appliance. At the same time, for applications such as energy feedback, Non-intrusive load monitoring (NILM) has been recognised as a scalable and practical alternative to submetering. However, the usability of NILM for anomaly detection has not yet been investigated.
Since the goal of NILM is to provide energy consumption estimate, it is unclear if the signal fidelity of appliance signatures generated by state-of-the-art NILM is sufficient to enable accurate appliance fault detection. In this paper, we attempt to determine whether appliance signatures detected by NILM can be used directly for anomaly detection. This is carried out by proposing an anomaly detection algorithm which performs well for submetering data and evaluate its ability to identify the same faulty behaviour of appliances but with NILM-generated appliance power traces. Our results on a dataset of six residential homes using four state-of-the-art NILM algorithms show that, on average, NILM traces are not as robust to identification of faulty behaviour as compared to using submetered data. We discuss in detail observations pertaining to the reconstructed appliance signatures following NILM and their fidelity with respect to noise-free submetered data.
Original languageEnglish
Pages (from-to)796-805
Number of pages10
JournalApplied Energy
Volume238
Early online date25 Jan 2019
DOIs
Publication statusPublished - 15 Mar 2019

Keywords

  • energy disaggregation
  • NILM
  • anomaly detection
  • smart meter

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