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

Tools

Rashid, Haroon and Singh, Pushpendra and Stankovic, Vladimir and Stankovic, Lina (2019) Can non-intrusive load monitoring be used for identifying an appliance's anomalous behaviour? Applied Energy, 238. pp. 796-805. ISSN 0306-2619 (https://doi.org/10.1016/j.apenergy.2019.01.061)

[thumbnail of Rashid-etal-AE2019-Can-non-intrusive-load-monitoring-be-used-for-identifying]
Preview
 Text. Filename: Rashid_etal_AE2019_Can_non_intrusive_load_monitoring_be_used_for_identifying.pdf
Accepted Author Manuscript
License: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 logo
Download (570kB)| Preview

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.

ORCID iDs

Rashid, Haroon, Singh, Pushpendra, Stankovic, Vladimir ORCID logoORCID: https://orcid.org/0000-0002-1075-2420 and Stankovic, Lina ORCID logoORCID: https://orcid.org/0000-0002-8112-1976;
CORE (COnnecting REpositories)