Juhi Ranjan
Erin Griffiths
Kamin Whitehouse
ABSTRACT
Energy auditing and feedback is an effective and low cost technique that has the potential to save 20-50% energy in homes. Several new sensing technologies can now detect and disaggregate energy usage in homes at a fixture level, which is helpful for eco-feedback in homes. However, without disaggregating and assigning fixture energy usage to individuals (fixture assignment problem), it is hard for residents to discover individual energy saving actions. In this paper, we explore the hypothesis that fixture assignment can be performed based on coarse-grained room-level location tracking -- even when a fixture is used and multiple people are in the same room. To test this hypothesis, we perform a study with 5 groups of 2 participants each, who lived together for 7-12 days in a test home. We find that fixture assignment can be performed with an average accuracy of 87% using room-level tracking. In comparison, fixture assignment has 12% accuracy with house-level tracking (who is home vs not home) and 97% accuracy with coordinate-level tracking (who is standing at the oven vs fridge).
- Y. Cheng, K. Chen, B. Zhang, C.-J. M. Liang, X. Jiang, and F. Zhao. Accurate real-time occupant energy-footprinting in commercial buildings. In Proceedings of the Fourth ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings, BuildSys '12. Google Scholar
Digital Library
- CSV-A8: AC Current Switch. http://www.onsetcomp.com/products/sensors/csv-a8.Google Scholar
- Energy Information Administration. 2009 Residential Energy Consumption Survey. http://www.eia.gov/consumption/residential/.Google Scholar
- M. Ester, H.-P. Kriegel, J. Sander, and X. Xu. A density-based algorithm for discovering clusters in large spatial databases with noise. In Kdd, volume 96, pages 226--231, 1996.Google ScholarDigital Library
- FTB4700: Low Flow Liquid Flowmeters. http://www.omega.com/pptst/FTB4700FTB4800.html.Google Scholar
- J. E. Froehlich, E. Larson, T. Campbell, C. Haggerty, J. Fogarty, and S. N. 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, 2009. Google ScholarDigital Library
- S. Gupta, M. S. Reynolds, and S. N. Patel. Electrisense: single-point sensing using emi for electrical event detection and classification in the home. In Proceedings of the 12th ACM international conference on Ubiquitous computing, pages 139--148. ACM, 2010. Google ScholarDigital Library
- A. Gustafsson and M. Gyllenswärd. The power-aware cord: energy awareness through ambient information display. In CHI'05 extended abstracts on Human factors in computing systems, pages 1423--1426. ACM, 2005. Google ScholarDigital Library
- M. Haakana, L. Sillanpaeae, and M. Talsi. The effect of feedback and focused advice on household energy consumption. 1997.Google Scholar
- S. Hay and A. Rice. The case for apportionment. In Proceedings of the First ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings, BuildSys '09, pages 13--18. ACM, 2009. Google ScholarDigital Library
- S. Hay, A. Rice, and A. Hopper. A global personal energy meter. In Adjunct Proceedings of the 7th International Conference on Pervasive Computing, 2009.Google Scholar
- UX90-002M: HOBO UX90 Light On/Off Logger with Extended Memory. http://www.onsetcomp.com/products/data-loggers/ux90-002m.Google Scholar
- M. R. Hodges and M. E. Pollack. An object-use fingerprint: The use of electronic sensors for human identification. In UbiComp 2007: Ubiquitous Computing, pages 289--303. Springer, 2007. Google ScholarDigital Library
- J. Hsu, P. Mohan, X. Jiang, J. Ortiz, S. Shankar, S. Dawson-Haggerty, and D. Culler. Hbci: Human-building-computer interaction. In Proceedings of the 2Nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building, BuildSys '10, 2010. Google ScholarDigital Library
- Y. Kim, T. Schmid, Z. M. Charbiwala, J. Friedman, and M. B. Srivastava. Nawms: nonintrusive autonomous water monitoring system. In Proceedings of the 6th ACM conference on Embedded network sensor systems, 2008. Google ScholarDigital Library
- Y. Kim, T. Schmid, Z. M. Charbiwala, and M. B. Srivastava. Viridiscope: design and implementation of a fine grained power monitoring system for homes. In Proceedings of the 11th international conference on Ubiquitous computing, pages 245--254. ACM, 2009. Google ScholarDigital Library
- S. Lee, D. Ahn, S. Lee, R. Ha, and H. Cha. Personalized energy auditor: Estimating personal electricity usage. In Pervasive Computing and Communications (PerCom), 2014 IEEE International Conference on.Google Scholar
- D. Petersen, J. Steele, and J. Wilkerson. Wattbot: a residential electricity monitoring and feedback system. In CHI'09 Extended Abstracts on Human Factors in Computing Systems, pages 2847--2852. ACM, 2009. Google ScholarDigital Library
- M. Philipose, K. P. Fishkin, M. Perkowitz, D. J. Patterson, D. Fox, H. Kautz, and D. Hahnel. Inferring activities from interactions with objects. Pervasive Computing, 2004. Google ScholarDigital Library
- J. Ranjan, Y. Yao, and K. Whitehouse. An RF Doormat for Tracking People's Room Locations. In Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing, pages 797--800. ACM, 2013. Google ScholarDigital Library
- V. Srinivasan, J. Stankovic, and K. Whitehouse. Using height sensors for biometric identification in multi-resident homes. In Pervasive Computing, pages 337--354. 2010. Google ScholarDigital Library
- V. Srinivasan, J. Stankovic, and K. Whitehouse. Fixturefinder: Discovering the existence of electrical and water fixtures. In Proceedings of the 12th international conference on Information processing in sensor networks, pages 115--128. ACM, 2013. Google ScholarDigital Library
- TED: The Energy Detective. http://www.theenergydetective.com/home.Google Scholar
- UX120-017M: HOBO 4-Channel Pulse Data Logger. http://www.onsetcomp.com/products/data-loggers/ux90-001.Google Scholar
- UX90-001: HOBO UX90 State Logger. http://www.onsetcomp.com/products/data-loggers/ux90-001.Google Scholar
Index Terms
- Discerning electrical and water usage by individuals in homes
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