Varick L. Erickson
Skip Abstract Section
Abstract
Heating, cooling and ventilation accounts for 35% energy usage in the United States. Currently, most modern buildings still condition rooms assuming maximum occupancy rather than actual usage. As a result, rooms are often over-conditioned needlessly. Thus, in order to achieve efficient conditioning, we require knowledge of occupancy. This article shows how real time occupancy data from a wireless sensor network can be used to create occupancy models, which in turn can be integrated into building conditioning system for usage-based demand control conditioning strategies. Using strategies based on sensor network occupancy model predictions, we show that it is possible to achieve 42% annual energy savings while still maintaining American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) comfort standards.
- ASHRAE. 2007a. ASHRAE standard 55: Thermal environmental conditions for human occupancy. American Society of Heating, Refrigeration and Air-Conditioning Engineers, Inc.Google Scholar
- ASHRAE. 2007b. ASHRAE standard 62.1: Ventilation for acceptable indoor air quality. American Society of Heating, Refrigeration and Air-Conditioning Engineers, Inc.Google Scholar
- ASHRAE. 2007c. ASHRAE standard 90.1: Energy standard for buildings except low-rise residential buildings. American Society of Heating, Refrigeration and Air-Conditioning Engineers, Inc.Google Scholar
- EIA. 2010. EIA-Energy Information Administration. http://www.eia.doe.gov/.Google Scholar
- DOE. 2012. DOE-2 - building energy analysis tool and cost analysis tool. http://www.doe2.com/DOE2.Google Scholar
- B. Abushakra and D. Claridge. 2001. Accounting for the occupancy variable in inverse building energy baselining models. In Proceedings of the 1st International Conference for Enhanced Building Operations (ICEBO 2001).Google Scholar
- Y. Agarwal, B. Balaji, S. Dutta, R. K. Gupta, and T. Weng. 2011. Duty-cycling buildings aggressively: The next frontier in HVAC control. In Proceedings of the 10th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN'11). ACM/IEEE, 246--257.Google Scholar
- M. J. Brandemuehl and J. E. Braun. 1999. The impact of demand-controlled and economizer ventilation strategies on energy use in buildings. ASHRAE Trans. 105, 2.Google Scholar
- B. Burke and M. Keeler. 2009. Fundamentals of Integrated Design for Sustainable Building. Wiley.Google Scholar
- R. H. Dodier, G. P. Henze, D. K. Tiller, and X. Guo. 2006. Building occupancy detection through sensor belief networks. Ener. Build. 38, 9, 1033--1043.Google ScholarCross Ref
- eQuest Building Energy Analysis Tool. http://www.doe2.com/.Google Scholar
- V. Erickson, S. Achleitner, and A. E. Cerpa. 2013. POEM: Power-efficient occupancy-based energy management system. In Proceedings of the 12th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN'13). ACM/IEEE, 203--216. Google ScholarDigital Library
- V. Erickson, M. A. Carreira-Perpinan, and A. Cerpa. 2011. OBSERVE: Occupancy-based system for efficient reduction of HVAC energy. In Proceedings of the 10th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN'11). ACM/IEEE, 258--269.Google Scholar
- V. Erickson and A. Cerpa. 2010. Occupancy-based demand response HVAC control strategy. In Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building (BuildSys'10). ACM, New York, 7--12. Google ScholarDigital Library
- V. L. Erickson, Y. Lin, A. Kamthe, R. Brahme, A. Cerpa, M. D. Sohn, and S. Narayanan. 2009. Energy efficient building environment control strategies using real-time occupancy measurements. In Proceedings of the 1st ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings (BuildSys'09) in conjunction with ACM SenSys'09. ACM, 19--24. Google ScholarDigital Library
- W. J. Fisk, D. Faulkner, and D. P. Sullivan. 2006. Accuracy of CO2 sensors in commercial buildings: A pilot study. Tech. Rep. LBNL-61862, Lawrence Berkeley National Laboratory.Google Scholar
- D. Herron, J. Eidsmore, R. O'Brien, and D. Leverenz. 1983. Use of simplified input for BLAST energy analysis. Tech. Rep. E-185. Construction Engineering Research Laboratory, U.S. Army, Corps of Engineers, Champaign, IL, 1--110.Google Scholar
- A. Kamthe, L. Jiang, M. Dudys, and A. Cerpa 2009. SCOPES: Smart cameras object position estimation system. In Proceedings of the 6th European Conference on Wireless Sensor Networks (EWSN'09). Springer-Verlag, Berlin, 279--295. Google ScholarDigital Library
- C. Liao and P. Barooah. 2011. A novel stochastic agent-based model of building occupancy. In Proceedings of the IEEE American Control Conference (ACC'11). IEEE, 2095--2100.Google Scholar
- R. Lienhart and J. Maydt. 2002. An extended set of Haar-like features for rapid object detection. In Proceedings of the IEEE International Conference on Image Processing (ICIP 2002). IEEE, I-900--I-903 vol. 1.Google ScholarCross Ref
- J. Lu, T. Sookoor, V. Srinivasan, G. Gao, B. Holben, J. Stankovic, E. Field, and K. Whitehouse. 2010. The smart thermostat: Using occupancy sensors to save energy in homes. In Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems (SenSys'10). ACM, 211--224. Google ScholarDigital Library
- B. W. Olesen and K. C. Parsons. 2002. Introduction to thermal comfort standards and to the proposed new version of EN ISO 7730. Ener. Build. 34, 6, 537--548.Google ScholarCross Ref
- OpenCV. http://opencvlibrary.sourceforge.net/.Google Scholar
- J. U. Pfafferott, S. Herkel, D. E. Kalz, and A. Zeuschner. 2007. Comparison of low-energy office buildings in summer using different thermal comfort criteria. Ener. Build. 39, 7, 750--757.Google ScholarCross Ref
- J. Ploennigs, B. Hensel, and K. Kabitzsch. 2010. Wireless, collaborative virtual sensors for thermal comfort. In Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building (BuildSys'10). ACM, New York, 79--84. Google ScholarDigital Library
- M. Schumann, A. Burillo, and N. Wilson. 2010. Predicting the desired thermal comfort conditions for shared offices. In Proceedings of the International Conference on Computing in Civil and Building Engineering (ICCCBE-10) (Nottingham, UK).Google Scholar
- J. Scott, A. J. B. Brush, J. Krumm, B. Meyers, M. Hazas, S. Hodges, and N. Villar. 2011. Preheat: Controlling home heating using occupancy prediction. In Proceedings of the 13th International Conference on Ubiquitous Computing (UbiComp 2011) (Beijing, China). 281--290. Google ScholarDigital Library
- P. Viola and M. Jones. 2001. Robust real-time object detection. In Int. J. Comput. Vis. 57, 2, 137--154. Google ScholarDigital Library
Index Terms
- Occupancy Modeling and Prediction for Building Energy Management
Recommendations
Optimal HVAC building control with occupancy prediction
BuildSys '14: Proceedings of the 1st ACM Conference on Embedded Systems for Energy-Efficient BuildingsBuildings account for about 41% of primary energy consumption and 75% of the electricity. Space heating, space cooling, and ventilation are the dominant end uses, accounting for 41% of all energy consumed in the buildings sector. Growing interest in ...
Occupancy based demand response HVAC control strategy
BuildSys '10: Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in BuildingHeating, cooling and ventilation accounts for 30% energy usage and for 50% of the electricity usage in the United States. Currently, most modern buildings still condition rooms assuming maximum occupancy rather than actual usage. As a result, rooms are ...
POEM: power-efficient occupancy-based energy management system
IPSN '13: Proceedings of the 12th international conference on Information processing in sensor networksBuildings account for 40% of US primary energy consumption and 72% of electricity. Of this total, 50% of the energy consumed in buildings is used for Heating Ventilation and Air-Conditioning (HVAC) systems. Current HVAC systems only condition based on ...
Comments