Evaluation of Non-intrusive Load Monitoring Algorithms for Appliance-level Anomaly Detection

Abstract

Appliance fault 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.

Anomaly detection for electricity consumption in cloud computing: framework, methods, applications, and challenges

“…Rashid et al [69] identified and defined factor models to reduce dimension. Furthermore, the author presented a new approach to estimate high-dimensional factor models, using the empirical spectral density of residuals.…”

“…Rashid et al [69] proposed a revised method based on the non-intrusive load monitoring theory. They constructed a workload model by using the REFIT dataset from smart-meter devices and regarded it as a built-in prediction model for the method.…”

A Practical Approach to Residential Appliances on-Line Anomaly Detection: A Case Study of Standard and Smart Refrigerators

“…Although, these systems have been thoroughly probed from both intrusive and non-intrusive aspects, their anomaly detection capability has not been fairly taken into consideration. In terms of Non-Intrusive Load Monitoring (NILM), few studies have only investigated the proficiency of load disaggregation methods for anomaly detection [12], [13]. Furthermore, in [11], we have aimed to design a NILM system for diagnosis purposes.…”

“…Besides, other studies have mainly examined the proficiency of NILM methods for anomaly detection. Rashid et al have evaluated the ability of household appliances load disaggregation techniques for anomaly detection [13]. Likewise, they have concluded that enhanced NILM algorithms are required to achieve such an ability.…”

“…Additionally, they have neither proposed an anomaly detection method nor presented numerical results. In [13], Rashid has also reported the anomaly detection based on sub-metered data. However, by utilizing a window length of one day, his analysis is more suitable for an off-line run.…”