Automatic Standby Power Management Using Usage Profiling and Prediction

Lee, Seung-Woo; Ryu, Gilyoung; Chon, Yohan; Ha, Rhan; Cha, Hojung

doi:10.1109/thms.2013.2285921

Cited by 34 publications

(17 citation statements)

References 15 publications

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“…Since these types of studies are based on large historical data sets, they do not reflect precise personalized consumption patterns. In another group of studies, activity recognition has been used for appliance standby mode or lighting system control [20][21][22]. Finally, there are a few studies that focus on possible applications of activity recognition for creating energy consumption awareness.…”

Section: Related Prior Workmentioning

confidence: 99%

A framework for allocating personalized appliance-level disaggregated electricity consumption to daily activities

Ahmadi-Karvigh

Becerik-Gerber

Soibelman

2016

Energy and Buildings

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Residential and commercial buildings account for more than 74% of total annual electricity consumption in the United States. Studies have shown that occupants' awareness of their behaviors in consuming electricity encourages them to change their unsustainable behaviors and improves the sustainable ones. As behaviors impact the ways that daily activities are performed, in order to develop a personalized appliance level model of an occupant's behavior, precise activity recognition is required. In this paper, we introduce a novel framework to allocate personalized appliance-level disaggregated electricity consumption to daily activities. In our framework, using ontology based approach, the input appliance usage data is first separated into categories of non-overlapping activity events. The separated data sets are then segmented to detect activity segments, which are next mapped into activity classes using a trained classification model. To evaluate the performance of our presented framework, an experimental validation was carried out in three test bed apartment units. Results of validation showed a total F-measure value of 0.97 for segmentation and an average accuracy of 93.41% for activity recognition. Following the activity recognition, the approximate electricity consumption associated with the recognized activities was estimated and the results of each test bed unit were compared with the others.

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Section: Related Prior Workmentioning

confidence: 99%

A framework for allocating personalized appliance-level disaggregated electricity consumption to daily activities

Ahmadi-Karvigh

Becerik-Gerber

Soibelman

2016

Energy and Buildings

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“…The next layer is composed of gateways that relay the information from the sensor up to the server. Gateways contains drivers and protocols enabling the communication with the sensors over potentially different networks such as PLC, IP, Enocean or EIB/KNX [18] [43]. The gateway layer is actually optional in the case, for example, of IP based sensors with a direct connection to the server.…”

Section: Appliance Monitoringmentioning

confidence: 99%

“…The server layer is in charge of communicating with the gateways and/or sensors, storing the data, processing the data and providing the necessary information to render vues in the upper layer. Examples of ILM systems have been described for appliance monitoring [13], appliance identification [41][29] [7] and consumption prediction [18].…”

Section: Appliance Monitoringmentioning

confidence: 99%

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A Survey on Intrusive Load Monitoring for Appliance Recognition

Ridi

Gisler

Hennebert

2014

2014 22nd International Conference on Pattern Recognition

129

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Electricity load monitoring of appliances has become an important task considering the recent economic and ecological trends. In this game, machine learning has an important part to play, allowing for energy consumption understanding, critical equipment monitoring and even human activity recognition. This paper provides a survey of current researches on Intrusive Load Monitoring (ILM) techniques. ILM relies on lowend electricity meter devices spread inside the habitations, as opposed to Non-Intrusive Load Monitoring (NILM) that relies on an unique point of measurement, the smart meter. Potential applications and principles of ILMs are presented and compared to NILM. A focus is also given on feature extraction and machine learning algorithms typically used for ILM applications.

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“…Additionally, the current regulation strategy of standby power typically focuses on real-power consumption, and it does not consider the apparent power and power factors. Lee and colleagues [63] propose an automatic standby power reduction system that is based on user-context profiling. The system profiles and analyzes the occupancy pattern, as well as the appliance usage.…”

Section: Activity Recognitionmentioning

confidence: 99%

Editorial IEEE Transactions on Human–Machine Systems: Year in Review for 2013

Bass

2014

IEEE Trans. Human-Mach. Syst.

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Automatic Standby Power Management Using Usage Profiling and Prediction

Cited by 34 publications

References 15 publications

A framework for allocating personalized appliance-level disaggregated electricity consumption to daily activities

A framework for allocating personalized appliance-level disaggregated electricity consumption to daily activities

A Survey on Intrusive Load Monitoring for Appliance Recognition

Editorial IEEE Transactions on Human–Machine Systems: Year in Review for 2013

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