Alex Beltran
ABSTRACT
In order to achieve sustainability, steps must be taken to reduce energy consumption. In particular, heating, cooling, and ventilation systems, which account for 42% of the energy consumed by US buildings in 2010 [8], must be made more efficient. In this paper, we demonstrate ThermoSense, a new system for estimating occupancy. Using this system we are able to condition rooms based on usage. Rather than fully conditioning empty or partially filled spaces, we can control ventilation based on near real-time estimates of occupancy and temperature using conditioning schedules learned from occupant usage patterns. ThermoSense uses a novel multisensor node that utilizes a low-cost, low-power thermal sensor array along with a passive infrared sensor. By using a novel processing pipeline and sensor fusion, we show that our system is able measure occupancy with a RMSE of only ≈0.35 persons. By conditioning spaces based on occupancy, we show that we can save 25% energy annually while maintaining room temperature effectiveness.
- Energyplus. http://apps1.eere.energy.gov/buildings/energyplus/.Google Scholar
- Grid-eye. http://pewa.panasonic.com/downloads/grid-eye/.Google Scholar
- Panasonic PIR. http://pewa.panasonic.com/downloads/papirs-ekmb/.Google Scholar
- Occupancy sensors, motion-sensing devices for lighting control. Lighting Research Center, 1998.Google Scholar
- ASHRAE standard 62.1: Ventilation for acceptable indoor air quality. ASHRAE, Inc., 2007.Google Scholar
- Y. Agarwal, B. Balaji, S. Dutta, R. K. Gupta, and T. Weng. Duty-cycling buildings aggressively: The next frontier in HVAC control. In IPSN'11.Google Scholar
- A. Bamis, D. Lymberopoulos, T. Teixeira, and A. Savvides. The behaviorscope framework for enabling ambient assisted living. Personal Ubiquitous Computing, 14(6):473--487, Sept. 2010. Google ScholarDigital Library
- DOE. 2010 Building Energy Data Book. U.S. Dept. of Energy, 2011.Google Scholar
- V. Erickson, S. Achleitner, and A. E. Cerpa. POEM: Power-efficient occupancy-based energy management system. In IPSN'13. Google ScholarDigital Library
- V. L. Erickson, M. Á. Carreira-Perpiñán, and A. E. Cerpa. OBSERVE: Occupancy-based system for efficient reduction of HVAC energy. In IPSN'11.Google Scholar
- W. J. Fisk. Accuracy of CO2 sensors in commercial buildings: a pilot study. Technical Report LBNL-61862, 2006.Google Scholar
- J. Hopcroft and R. Tarjan. Algorithm 447: efficient algorithms for graph manipulation. Commun. ACM, 16(6):372--378, June 1973. Google ScholarDigital Library
- A. Kamthe, L. Jiang, M. Dudys, and A. Cerpa. SCOPES: Smart cameras object position estimation system. In EWSN'09. Google ScholarDigital Library
- K. P. Lam, M. Hoynck, B. Dong, B. Andrews, Y.-S. Chiou, R. Chang, D. Benitez, and J. Choi. Occupancy detection through an extensive environmental sensor network in an open-plan office building. In International Building Performace Simulation Association, 2009.Google Scholar
- J. Lu, T. Sookoor, V. Srinivasan, G. Gao, B. Holben, J. Stankovic, E. Field, and K. Whitehouse. The smart thermostat: using occupancy sensors to save energy in homes. In SenSys 10. Google ScholarDigital Library
- S. Mamidi, R. Maheswaran, and Y. Chang. Smart sensing, estimation, and prediction for efficient building energy management. In Multi-agent Smart Computing Workshop, 2011.Google Scholar
- T. M. Mitchell. Machine Learning. McGraw-Hill, New York, 1997. Google ScholarDigital Library
- J. Scott, A. J. B. Brush, J. Krumm, B. Meyers, M. Hazas, S. Hodges, and N. Villar. Preheat: controlling home heating using occupancy prediction. In UbiComp 11. Google ScholarDigital Library
- T. Sky. http://www.snm.ethz.ch/Projects/TmoteSky.Google Scholar
- T. Teixeira and A. Savvides. Lightweight people counting and localization in indoor spaces using camera sensor nodes. In ICDSC'07.Google Scholar
Index Terms
- ThermoSense: Occupancy Thermal Based Sensing for HVAC Control
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