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Occupancy-driven energy management for smart building automation

Published:02 November 2010Publication History

ABSTRACT

Buildings are among the largest consumers of electricity in the US. A significant portion of this energy use in buildings can be attributed to HVAC systems used to maintain comfort for occupants. In most cases these building HVAC systems run on fixed schedules and do not employ any fine grained control based on detailed occupancy information. In this paper we present the design and implementation of a presence sensor platform that can be used for accurate occupancy detection at the level of individual offices. Our presence sensor is low-cost, wireless, and incrementally deployable within existing buildings. Using a pilot deployment of our system across ten offices over a two week period we identify significant opportunities for energy savings due to periods of vacancy. Our energy measurements show that our presence node has an estimated battery lifetime of over five years, while detecting occupancy accurately. Furthermore, using a building simulation framework and the occupancy information from our testbed, we show potential energy savings from 10% to 15% using our system.

References

  1. Y. Agarwal, S. Hodges, R. Chandra, J. Scott, P. Bahl, and R. Gupta. Somniloquy: Augmenting Network Interfaces to Reduce PC Energy Usage. In Proceedings of USENIX Symposium on Networked Systems Design and Implementation (NSDI '09), 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Y. Agarwal, S. Savage, and R. Gupta. SleepServer: A Software-Only Approach for Reducing the Energy Consumption of PCs within Enterprise Environments. In Proceedings of USENIX Annual Technical Symposium (USENIX ATC '10), 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Y. Agarwal, T. Weng, and R. Gupta. The Energy Dashboard: Improving the Visibility of Energy Consumption at a Campus-Wide Scale. In First ACM Workshop on Embedded Sensing Systems For Energy-Efficiency In Buildings, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. ASHRAE. ASHRAE Standard 90.1--2004.Google ScholarGoogle Scholar
  5. A. Barbato, L. Borsani, A. Capone, and S. Melzi. Home energy saving through a user profiling system based on wireless sensors. Conference On Embedded Networked Sensor Systems, pages 49--54, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. D. Crawley, J. Hand, M. Kummert, and B. Griffith. Comparison of Capabilities of 20 Building Simulation Programs.Google ScholarGoogle Scholar
  7. D. Crawley, L. Lawrie, and et. al. Energyplus, a new-generation building energy simulation program. In Proceedings of the Renewable and Advanced Energy Systems for the 21st Century, April 1999.Google ScholarGoogle Scholar
  8. D. T. Delaney, G. M. P. O'Hare, and A. G. Ruzzelli. Evaluation of energy-efficiency in lighting systems using sensor networks. Proceedings of the First ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings, pages 61--66, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. DOE. Buildings Energy Data Book, Department of Energy, March 2009. http://buildingsdatabook.eren.doe.gov/.Google ScholarGoogle Scholar
  10. V. L. Erickson, Y. Lin, A. Kamthe, R. Brahme, A. Surana, A. E. Cerpa, M. D. Sohn, and S. Narayanan. Energy efficient building environment control strategies using real-time occupancy measurements. Proceedings of the First ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings, pages 19--24, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. D. Estrin, L. Girod, G. Pottie, and M. Srivastava. Instrumenting The World With Wireless Sensor Networks. In International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2001), pages 2033--2036, 2001.Google ScholarGoogle Scholar
  12. G. Gao and K. Whitehouse. The self-programming thermostat: optimizing setback schedules based on home occupancy patterns. Proceedings of the First ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings, pages 67--72, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Honeywell. Honeywell WSK-24 Wireless Occupancy Solution. http://specifyhoneywell.com/customer/techlit/pdf/63-0000s/63-4519.pdf.Google ScholarGoogle Scholar
  14. J. Kleissl and Y. Agarwal. Cyber-Physical Energy Systems: Focus on Smart Buildings. In Proceedings of the ACM/EDAC/IEEE Design Automation Conference, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. L. J. Lo and A. Novoselac. Localized air-conditioning with occupancy control in an open office. Energy and Buildings, 2010.Google ScholarGoogle ScholarCross RefCross Ref
  16. K. Padmanabh, A. M. V, S. Sen, S. P. Katru, A. Kumar, S. P. C, S. K. Vuppala, and S. Paul. isense: A wireless sensor network based conference room management system. Proceedings of the First ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings, pages 37--42, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. S. P. Tarzia, R. P. Dick, P. A. Dinda, and G. Memik. Sonar-based measurement of user presence and attention. UbiComp, pages 89--92, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. T. Teixeira and A. Savvides. Lightweight people counting and localizing for easily deployable indoors wsns. IEEE Journal of Selected Topics in Signal Processing, 2(4):493--502, August 2008.Google ScholarGoogle ScholarCross RefCross Ref
  19. S. Wang and X. Jin. Co 2-based occupancy detection for on-line outdoor air flow control. Indoor and Built Environment, 7(3):165--181, 1998.Google ScholarGoogle ScholarCross RefCross Ref
  20. Y. Zhu, M. Liu, and et. al. Optimization of Control Strategies for HVAC Terminal Boxes. In Proceedings of 12th Symposium on Improving Building Systems in Hot and Humid Climates.Google ScholarGoogle Scholar

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        • Published in

          cover image ACM Conferences
          BuildSys '10: Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building
          November 2010
          93 pages
          ISBN:9781450304580
          DOI:10.1145/1878431

          Copyright © 2010 ACM

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          Publication History

          • Published: 2 November 2010

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          Overall Acceptance Rate148of500submissions,30%

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