Vijay Srinivasan
Kamin Whitehouse
ABSTRACT
The monitoring of electrical and water fixtures in the home is being applied for a variety of "smart home" applications, such as recognizing activities of daily living (ADLs) or conserving energy or water usage. Fixture monitoring techniques generally fall into two categories: fixture recognition and fixture disaggregation. However, existing techniques require users to explicitly identify each individual fixture, either by placing a sensor on it or by manually creating training data for it. In this paper, we present a new fixture discovery system that automatically infers the existence of electrical and water fixtures in the home. We call the system FixtureFinder. The basic idea is to use data fusion between the smart meters and other sensors or infrastructure already in the home, such as the home security or automation system, and to find repeating patterns in the fused data stream. To evaluate FixtureFinder, we deployed the system into 4 different homes for 7-10 days of data collection. Our results show that FixtureFinder is able to identify and differentiate major light and water fixtures in less than 10 days, including multiple copies of light bulbs and sinks that have identical power/water profiles.
- Pike research smart grid deployment tracker. http://www.pikeresearch.com/research/smart-grid-deployment-tracker-3q10.Google Scholar
- S. Barker, A. Mishra, D. Irwin, E. Cecchet, P. Shenoy, and J. Albrecht. Smart*: An open data set and tools for enabling research in sustainable homes. The 1st KDD Workshop on Data Mining Applications in Sustainability (SustKDD), 2011.Google Scholar
- C. Beckel, W. Kleiminger, T. Staake, and S. Santini. Improving device-level electricity consumption breakdowns in private households using on/o events. SIGBED Rev., 9(3), 2012. Google ScholarDigital Library
- J. Canny. A computational approach to edge detection. Pattern Analysis and Machine Intelligence, IEEE Transactions on, (6):679--698, 1986. Google ScholarDigital Library
- G. Cohn, S. Gupta, J. Froehlich, E. Larson, and S. N. Patel. Gassense: appliance-level, single-point sensing of gas activity in the home. In Proceedings of the 8th international conference on Pervasive Computing, Pervasive'10, 2010. Google ScholarDigital Library
- J. Fogarty, C. Au, and S. Hudson. Sensing from the basement: a feasibility study of unobtrusive and low-cost home activity recognition. In Proceedings of the 19th annual ACM symposium on User interface software and technology, pages 91--100. ACM, 2006. Google ScholarDigital Library
- J. Froehlich, E. Larson, T. Campbell, C. Haggerty, J. Fogarty, and S. Patel. HydroSense: infrastructure-mediated single-point sensing of whole-home water activity. In Proceedings of the 11th international conference on Ubiquitous computing, pages 235--244. ACM, 2009. Google ScholarDigital Library
- J. Froehlich, E. Larson, T. Campbell, C. Haggerty, J. Fogarty, and S. Patel. Hydrosense: infrastructure-mediated single-point sensing of whole-home water activity. In Proc. UbiComp, volume 9, pages 235--244. Citeseer, 2009. Google ScholarDigital Library
- H. Goncalves, A. Ocneanu, and M. Berges. Unsupervised disaggregation of appliances using aggregated consumption data. The 1st KDD Workshop on Data Mining Applications in Sustainability (SustKDD), 2011.Google Scholar
- S. Gupta, M. Reynolds, and S. Patel. Electrisense: Single-point sensing using emi for electrical event detection and classification in the home. In Proceedings of Ubicomp, 2010. Google ScholarDigital Library
- G. Hart. Nonintrusive appliance load monitoring. Proceedings of the IEEE, 80(12):1870--1891, 1992.Google ScholarCross Ref
- L. Heyer, S. Kruglyak, and S. Yooseph. Exploring expression data: identification and analysis of coexpressed genes. Genome research, 9(11):1106, 1999.Google ScholarCross Ref
- X. Jiang, S. Dawson-Haggerty, P. Dutta, and D. Culler. Design and implementation of a high-fidelity ac metering network. In Proceedings of IPSN, 2009. Google ScholarDigital Library
- D. Jung and A. Savvides. Estimating building consumption breakdowns using on/o state sensing and incremental sub-meter deployment. In SenSys, pages 225--238, 2010. Google ScholarDigital Library
- Y. Kim, R. Balani, H. Zhao, and M. B. Srivastava. Granger causality analysis on ip traffic and circuit-level energy monitoring. In Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building, BuildSys '10, pages 43--48, 2010. Google ScholarDigital Library
- Y. Kim, T. Schmid, Z. Charbiwala, J. Friedman, and M. Srivastava. Nawms: nonintrusive autonomous water monitoring system. In Proceedings of the 6th ACM conference on Embedded network sensor systems, pages 309--322. ACM, 2008. Google ScholarDigital Library
- Y. Kim, T. Schmid, Z. Charbiwala, and M. Srivastava. Viridiscope: design and implementation of a fine grained power monitoring system for homes. In Proceedings of Ubicomp, 2009. Google ScholarDigital Library
- J. Z. Kolter and M. J. Johnson. Redd: A public data set for energy disaggregation research. The 1st KDD Workshop on Data Mining Applications in Sustainability (SustKDD), 2011.Google Scholar
- H. Kuhn. The hungarian method for the assignment problem. Naval research logistics quarterly, 2(1-2):83--97, 1955.Google Scholar
- P. Mayer and W. DeOreo. Residential end uses of water. American Water Works Association, 1999.Google Scholar
- Parks Associates Research and Analysis for Digital Living. Home security system forecasts: 2005 and beyond, November 2005.Google Scholar
- O. Parson, S. Ghosh, M. Weal, and A. Rogers. Non-intrusive load monitoring using prior models of general appliance types. AAAi, 2012.Google ScholarDigital Library
- S. Patel, M. Reynolds, and G. Abowd. Detecting human movement by di erential air pressure sensing in hvac system ductwork: An exploration in infrastructure mediated sensing. In Pervasive Computing, Lecture Notes in Computer Science, 2008. Google ScholarDigital Library
- S. Patel, T. Robertson, J. Kientz, M. Reynolds, and G. Abowd. At the ick of a switch: Detecting and classifying unique electrical events on the residential power line. Lecture Notes in Computer Science, 4717:271, 2007. Google ScholarDigital Library
- J. Polastre, R. Szewczyk, and D. Culler. Telos: enabling ultra-low power wireless research. In Proceedings of IPSN, 2005. Google ScholarDigital Library
- A. Rowe, M. Berges, and R. Rajkumar. Contactless sensing of appliance state transitions through variations in electromagnetic fields. In Proceedings of Buildsys, 2010. Google ScholarDigital Library
- V. Srinivasan, J. Stankovic, and K. Whitehouse. Watersense: Water ow disaggregation using motion sensors. In The 3rd ACM Workshop On Embedded Sensing Systems For Energy-Efficiency In Buildings (BuildSys), in conjunction with ACM SenSys, 2011. Google ScholarDigital Library
Index Terms
- FixtureFinder: discovering the existence of electrical and water fixtures
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