Multi-User Energy Consumption Monitoring and Anomaly Detection with Partial Context Information

Arjunan, Pandarasamy; Khadilkar, Harshad; Ganu, Tanuja; Charbiwala, Zainul; Singh, Amarjeet; Singh, Pushpendra

doi:10.1145/2821650.2821662

Cited by 34 publications

(25 citation statements)

References 10 publications

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“…By considering that the condition x * k(t) = 0 implies that participant k(t) is defaulting, the above operation is direct load control of a defaulting participant. Note that Algorithm 2 is executed with (12) and (13). Note also thatx * i (0) is unknown to the aggregator before executing the algorithm because x * i is unknown.…”

Section: A Direct Load Control For Defaulting Participantsmentioning

confidence: 99%

Detection of Defaulting Participants of Demand Response Based on Sparse Reconstruction

Azuma

Sato

Kobayashi

et al. 2020

IEEE Trans. Smart Grid

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Section: A Direct Load Control For Defaulting Participantsmentioning

confidence: 99%

Detection of Defaulting Participants of Demand Response Based on Sparse Reconstruction

Azuma

Sato

Kobayashi

et al. 2020

IEEE Trans. Smart Grid

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“…However, such approaches are cumbersome as separate intrusive data collection kit is required for each appliance in a home. [1,2,3] propose different anomaly detection approaches using aggregate smart meter data. These approaches detect anomalies but are unable to identify the anomaly causing appliance.…”

Section: Related Workmentioning

confidence: 99%

“…The anomaly detection problem is well investigated, and with the emergence of smart grids and widespread use of smart meters, load anomaly detection continues to remain in the research focus [1,2,3]. Smart meters, measuring aggregate household consumption, allow online billing, facilitate demand response measures, and home automation by logging energy consumption data at frequencies often in the order of seconds.…”

Section: Introductionmentioning

confidence: 99%

“…Smart meters, measuring aggregate household consumption, allow online billing, facilitate demand response measures, and home automation by logging energy consumption data at frequencies often in the order of seconds. Existing smart meter-based anomaly detection approaches only detect anomalies at aggregate, household-level and does not identify the anomaly causing appliance [1,2,3]. Identifying timely anomalous appliances can reduce energy wastage and appliance breakdown time [4]; however, so far, it has required the use of submetered data at appliance level, which is a massive overhead due to a combination of installation, maintenance, communications, storage, and data validity checks.…”

Section: Introductionmentioning

confidence: 99%

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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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“…In [56], a lightweight human activity classification has been performed to identify human activities using smartphones and iBeacon sensor. Multi-user energy consumption monitoring and anomaly detection has been proposed in [51] augmenting the users and environmental contextual information.…”

Section: Related Workmentioning

confidence: 99%

Fine-grained appliance usage and energy monitoring through mobile and power-line sensing

Roy

Pathak

Misra

2016

Pervasive and Mobile Computing

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To promote energy-efficient operations in residential and office buildings, non-intrusive load monitoring (NILM) techniques have been proposed to infer the fine-grained power consumption and usage patterns of appliances from power-line measurement data. Fine-grained monitoring of everyday appliances (such as toasters and coffee makers) can not only promote energy-efficient building operations, but also provide unique insights into the context and activities of individuals. Current building-level NILM techniques are unable to identify the consumption characteristics of relatively low-load appliances, whereas smart-plug based solutions incur significant deployment and maintenance costs. In this paper, we investigate an intermediate architecture, where smart circuit breakers provide measurements of aggregate power consumption at room (or section) level granularity. We then investigate techniques to identify the usage and energy consumption of individual appliances from such measurements. We first develop a novel correlation-based approach called CBPA to identify individual appliances based on both their unique transient and steady-state power signatures. While promising, CBPA fails when the set of candidate appliances is too large. To further improve the accuracy of appliance level usage estimation, we then propose a hybrid system called AARPA, which uses mobile sensing to first infer high-level activities of daily living (ADLs), and then uses knowledge of such ADLs to effectively reduce the set of candidate appliances that potentially contribute to the aggregate readings at any point. We evaluate two variants of this algorithm, and show, using real-life data traces gathered from 10 domestic users, that our fusion of mobile and power-line sensing is very promising: it identified all devices that were used in each data trace, and it identified the usage duration and energy consumption of low-load consumer appliances with ∼ 87% accuracy.

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Multi-User Energy Consumption Monitoring and Anomaly Detection with Partial Context Information

Cited by 34 publications

References 10 publications

Detection of Defaulting Participants of Demand Response Based on Sparse Reconstruction

Detection of Defaulting Participants of Demand Response Based on Sparse Reconstruction

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

Fine-grained appliance usage and energy monitoring through mobile and power-line sensing

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