Unsupervised Detection of Unusual Behaviors from Smart Home Energy Data

Pereira, Welma; Ferscha, Alois; Weigl, Klemens

doi:10.1007/978-3-319-39384-1_46

Cited by 5 publications

(6 citation statements)

References 21 publications

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“…In Reference [39], the authors design device‐specific power consumption models for a set of electrical appliances and thereby utilize developed models to detect abnormalities for each appliance operation cycle using its specific setting parameters. In a similar way, a clustering approach is implemented in Reference [40], which aims at detecting anomalous consumption via the comparison of device consumption fingerprints with developed models through various periods of time.…”

Section: Related Workmentioning

confidence: 99%

Smart power consumption abnormality detection in buildings using micromoments and improved K‐nearest neighbors

et al. 2021

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Anomaly detection in energy consumption is a crucial step towards developing efficient energy saving systems, diminishing overall energy expenditure and reducing carbon emissions. Therefore, implementing powerful techniques to identify anomalous consumption in buildings and providing this information to end‐users and managers is of significant importance. Accordingly, two novel schemes are proposed in this paper; the first one is an unsupervised abnormality detection based on one‐class support vector machine, namely UAD‐OCSVM, in which abnormalities are extracted without the need of annotated data; the second is a supervised abnormality detection based on micromoments (SAD‐M2), which is implemented in the following steps: (i) normal and abnormal power consumptions are defined and assigned; (ii) a rule‐based algorithm is introduced to extract the micromoments representing the intent‐rich moments, in which the end‐users make decisions to consume energy; and (iii) an improved K‐nearest neighbors model is introduced to automatically classify consumption footprints as normal or abnormal. Empirical evaluation conducted in this framework under three different data sets demonstrates that SAD‐M2 achieves both a highest abnormality detection performance and real‐time processing capability with considerably lower computational cost in comparison with other machine learning methods. For instance, up to 99.71% accuracy and 99.77% F1 score have been achieved using a real‐world data set collected at the Qatar University energy lab.

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Section: Related Workmentioning

confidence: 99%

Smart power consumption abnormality detection in buildings using micromoments and improved K‐nearest neighbors

et al. 2021

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“…Submetered data is used to build models specific for AC, washing machine, and refrigerator which track appliance's consumption over time and flag anomalous usage instances [1]. A self-adaptive stream clustering algorithm [2] is proposed to detect anomalies in the previous appliances as well as in electronic loads (TV, Laptop, Tablet, Mobile phone) using submetered data from these appliances. However, multiple appliance-level monitors are needed, which impacts the scalability of these approaches [1,2,3].…”

Section: Related Workmentioning

confidence: 99%

“…A self-adaptive stream clustering algorithm [2] is proposed to detect anomalies in the previous appliances as well as in electronic loads (TV, Laptop, Tablet, Mobile phone) using submetered data from these appliances. However, multiple appliance-level monitors are needed, which impacts the scalability of these approaches [1,2,3].…”

Section: Related Workmentioning

confidence: 99%

“…Identifying faulty behaviour of appliances in buildings has traditionally used submetered data, i.e., measuring energy consumption at appliance level individually, as in [1,2,3]. However, the number of submeters or individual appliance monitors increases with the number of appliances or loads, and therefore anomaly detection based on submetering is not a scalable solution, especially in modern households with over 40 electric appliances 1 .…”

Section: Introductionmentioning

confidence: 99%

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

2019

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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

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“…For example, Ganu et al [10] first create appliance specific energy models of several high energy consuming appliances and then use built models to identify anomalies. Similarly, Pereira et al [11] use clustering to identify anomalies by comparing the usage of an appliance along a period. However, such approaches are cumbersome as separate intrusive data collection kit is required for each appliance in a home.…”

Section: Related Workmentioning

confidence: 99%

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

Rashid

Stanković

Singh

2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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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.

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Unsupervised Detection of Unusual Behaviors from Smart Home Energy Data

Cited by 5 publications

References 21 publications

Smart power consumption abnormality detection in buildings using micromoments and improved K‐nearest neighbors

Smart power consumption abnormality detection in buildings using micromoments and improved K‐nearest neighbors

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

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

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