Evaluation of non-intrusive load monitoring algorithms for appliance-level anomaly detection

Haroon Rashid, Vladimir Stankovic, Lina Stankovic, Pushpendra Singh

Research output: Contribution to conferencePaper

Abstract

Appliance faults in buildings resulting in abnormal energy consumption is known as an anomaly. Traditionally, anomaly detection is performed either at aggregate, i.e., meter-level, or at appliance level. Meter-level anomaly detection does not identify the anomaly-causing appliance, while appliance-level detection requires submetering each appliance in the building. Non-Intrusive Load Monitoring (NILM) has been proposed as an alternative to submetering to detect when appliances are running as well as estimate the appliance energy consumption. So far, applications have revolved around meaningful energy feedback. In this paper, we assess whether NILM can indeed be used for anomaly detection, as an alternative to submetering. We propose a supervised anomaly detection approach, AEM, and evaluate the effectiveness of NILM for anomaly detection. The proposed approach first learns an appliance's normal operation and then monitors its energy consumption for anomaly detection. We resort to real data, aggregate and submetered data from the two-year long REFIT dataset. We explain why anomaly detection performs worse with NILM data as compared to submetered data, highlighting the need for new, anomaly-aware NILM approaches.

Conference

Conference2019 IEEE International Conference on Acoustics, Speech, and Signal Processing
Abbreviated titleICASSP
CountryUnited Kingdom
CityBrighton
Period12/05/1917/05/19
Internet address

Fingerprint

Monitoring
Energy utilization
Feedback

Keywords

  • NILM
  • energy disaggregation
  • anomaly detection
  • smart metering

Cite this

Rashid, H., Stankovic, V., Stankovic, L., & Singh, P. (Accepted/In press). Evaluation of non-intrusive load monitoring algorithms for appliance-level anomaly detection. Paper presented at 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, Brighton, United Kingdom.
Rashid, Haroon ; Stankovic, Vladimir ; Stankovic, Lina ; Singh, Pushpendra. / Evaluation of non-intrusive load monitoring algorithms for appliance-level anomaly detection. Paper presented at 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, Brighton, United Kingdom.5 p.
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Rashid, H, Stankovic, V, Stankovic, L & Singh, P 2019, 'Evaluation of non-intrusive load monitoring algorithms for appliance-level anomaly detection' Paper presented at 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, Brighton, United Kingdom, 12/05/19 - 17/05/19, .

Evaluation of non-intrusive load monitoring algorithms for appliance-level anomaly detection. / Rashid, Haroon; Stankovic, Vladimir; Stankovic, Lina; Singh, Pushpendra.

2019. Paper presented at 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, Brighton, United Kingdom.

Research output: Contribution to conferencePaper

TY - CONF

T1 - Evaluation of non-intrusive load monitoring algorithms for appliance-level anomaly detection

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AU - Stankovic, Lina

AU - Singh, Pushpendra

N1 - © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

PY - 2019/2/1

Y1 - 2019/2/1

N2 - Appliance faults in buildings resulting in abnormal energy consumption is known as an anomaly. Traditionally, anomaly detection is performed either at aggregate, i.e., meter-level, or at appliance level. Meter-level anomaly detection does not identify the anomaly-causing appliance, while appliance-level detection requires submetering each appliance in the building. Non-Intrusive Load Monitoring (NILM) has been proposed as an alternative to submetering to detect when appliances are running as well as estimate the appliance energy consumption. So far, applications have revolved around meaningful energy feedback. In this paper, we assess whether NILM can indeed be used for anomaly detection, as an alternative to submetering. We propose a supervised anomaly detection approach, AEM, and evaluate the effectiveness of NILM for anomaly detection. The proposed approach first learns an appliance's normal operation and then monitors its energy consumption for anomaly detection. We resort to real data, aggregate and submetered data from the two-year long REFIT dataset. We explain why anomaly detection performs worse with NILM data as compared to submetered data, highlighting the need for new, anomaly-aware NILM approaches.

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Rashid H, Stankovic V, Stankovic L, Singh P. Evaluation of non-intrusive load monitoring algorithms for appliance-level anomaly detection. 2019. Paper presented at 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, Brighton, United Kingdom.