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dc.contributor.authorRoy, Nirmalya
dc.contributor.authorPathak, Nilavra
dc.contributor.authorMisra, Archan
dc.date.accessioned2018-09-04T20:19:07Z
dc.date.available2018-09-04T20:19:07Z
dc.date.issued2015-09-14
dc.description© 2015 IEEE; 2015 16th IEEE International Conference on Mobile Data Managementen_US
dc.description.abstractTo 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.en_US
dc.description.sponsorshipThis work is supported partially by the NSF Award #1344990, and Constellation E2: Energy to Educate Grant. We would like to thank Dr. Behrooz Shirazi for his valuable comments and feedback on this work.en_US
dc.description.urihttp://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7264324&isnumber=7264280en_US
dc.format.extent6 PAGESen_US
dc.genreconference papers and proceedings preprintsen_US
dc.identifierdoi:10.13016/M26W96C9N
dc.identifier.citationN. Roy, N. Pathak and A. Misra, "AARPA: Combining Mobile and Power-Line Sensing for Fine-Grained Appliance Usage and Energy Monitoring," 2015 16th IEEE International Conference on Mobile Data Management, Pittsburgh, PA, 2015, pp. 213-218.en_US
dc.identifier.uri10.1109/MDM.2015.64
dc.identifier.urihttp://hdl.handle.net/11603/11224
dc.language.isoen_USen_US
dc.publisherIEEEen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Information Systems Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.rightsThis item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please contact the author.
dc.subjectPower demanden_US
dc.subjectCircuit breakersen_US
dc.subjectRefrigeratorsen_US
dc.subjectEnergy consumptionen_US
dc.subjectIronen_US
dc.subjectMobile communicationen_US
dc.subjectMobile Pervasive & Sensor Computing Lab
dc.titleAARPA: Combining Mobile and Power-line Sensing for Fine-grained Appliance Usage and Energy Monitoringen_US
dc.typeTexten_US


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