Proceedings of the 2nd ACM International Conference on Embedded Systems for Energy-Efficient Built Environments 2015
DOI: 10.1145/2821650.2821662
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Multi-User Energy Consumption Monitoring and Anomaly Detection with Partial Context Information

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Cited by 32 publications
(25 citation statements)
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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%
“…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%
“…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%
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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%