Omid Ardakanian
ABSTRACT
The design of energy-efficient commercial building Heating Ventilation and Air Conditioning (HVAC) systems has been in the forefront of energy conservation efforts over the past few decades. The HVAC systems traditionally run on a static schedule that does not take occupancy into account, wasting a lot of energy in conditioning empty or partially-occupied spaces. This paper investigates the application of non-intrusive techniques to obtain a rough estimate of occupancy from coarse-grained measurements of the sensors that are commonly available through the building management system. Various per-zone schedules can be developed based on this approximate knowledge of occupancy at the level of individual zones. Our experiments in three large commercial buildings confirm that the proposed techniques can uncover the occupancy pattern of the zones, and schedules that incorporate these occupancy patterns can achieve more than 38% reduction in reheat energy consumption while maintaining indoor thermal comfort.
Supplemental Material
- Y. Agarwal, B. Balaji, S. Dutta, R. K. Gupta, and T. Weng. Duty-cycling buildings aggressively: The next frontier in HVAC control. In IPSN, pages 246--257, April 2011.Google Scholar
- ASHRAE. Standard 90.1-2013. https://www.ashrae.org/resources--publications/bookstore/standard-90-1.Google Scholar
- A. Aswani, N. Master, J. Taneja, V. Smith, A. Krioukov, D. Culler, and C. Tomlin. Identifying models of HVAC systems using semiparametric regression. In American Control Conference (ACC), pages 3675--3680, June 2012.Google ScholarCross Ref
- B. Balaji, J. Xu, A. Nwokafor, R. Gupta, and Y. Agarwal. Sentinel: Occupancy based HVAC actuation using existing wifi infrastructure within commercial buildings. In SenSys, pages 17:1--17:14. ACM, 2013. Google ScholarDigital Library
- A. Beltran, V. L. Erickson, and A. E. Cerpa. Thermosense: Occupancy thermal based sensing for HVAC control. In BuildSys, pages 11:1--11:8. ACM, 2013. Google ScholarDigital Library
- J. Canny. A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell., 8(6):679--698, June 1986. Google ScholarDigital Library
- D. Chen, S. Barker, A. Subbaswamy, D. Irwin, and P. Shenoy. Non-intrusive occupancy monitoring using smart meters. In BuildSys, pages 9:1--9:8. ACM, 2013. Google ScholarDigital Library
- D. B. Crawley, C. O. Pedersen, L. K. Lawrie, and F. C. Winkelmann. EnergyPlus: Energy simulation program. ASHRAE Journal, 42:49--56, 2000.Google Scholar
- CUErgo. Ambient environment: Thermal conditions. http://ergo.human.cornell.edu/studentdownloads/DEA3500notes/Thermal/thcondnotes.html, 2016, retrieved.Google Scholar
- B. Dong and K. P. Lam. Building energy and comfort management through occupant behaviour pattern detection based on a large-scale environmental sensor network. Building Performance Simulation, 4(4):359--369, 2011.Google ScholarCross Ref
- A. Ebadat, G. Bottegal, D. Varagnolo, B. Wahlberg, and K. H. Johansson. Regularized deconvolution-based approaches for estimating room occupancies. IEEE Transactions on Automation Science and Engineering, 12(4):1157--1168, Oct 2015.Google ScholarCross Ref
- V. L. Erickson, S. Achleitner, and A. E. Cerpa. POEM: Power-efficient occupancy-based energy management system. In IPSN, pages 203--216. ACM, 2013. Google ScholarDigital Library
- R. Fontugne, J. Ortiz, N. Tremblay, P. Borgnat, P. Flandrin, K. Fukuda, D. Culler, and H. Esaki. Strip, bind, and search: A method for identifying abnormal energy consumption in buildings. In IPSN, pages 129--140. ACM, 2013. Google ScholarDigital Library
- S. K. Ghai, L. V. Thanayankizil, D. P. Seetharam, and D. Chakraborty. Occupancy detection in commercial buildings using opportunistic context sources. In Pervasive Computing and Communications Workshops, IEEE International Conference on, pages 463--466, March 2012.Google ScholarCross Ref
- N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng, N.-C. Yen, C. C. Tung, and H. H. Liu. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 454(1971):903--995, 1998.Google ScholarCross Ref
- M. Jin, N. Bekiaris-Liberis, K. Weekly, C. Spanos, and A. Bayen. Sensing by proxy: Occupancy detection based on indoor CO2 concentration. In Proceedings of the 9th International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies, pages 1--10, 2015.Google Scholar
- A. Kamthe, V. Erickson, M. A. Carreira-Perpiñán, and A. Cerpa. Enabling building energy auditing using adapted occupancy models. In BuildSys, pages 31--36. ACM, 2011. Google ScholarDigital Library
- W. Kleiminger, C. Beckel, and S. Santini. Household occupancy monitoring using electricity meters. In UbiComp, pages 975--986. ACM, 2015. Google ScholarDigital Library
- V. B. Krishna, D. Jung, N. Q. M. Khiem, H. H. Nguyen, and D. K. Y. Yau. Energytrack: Sensor-driven energy use analysis system. In BuildSys, pages 38:1--38:2. ACM, 2013. Google ScholarDigital Library
- 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, pages 211--224. ACM, 2010. Google ScholarDigital Library
- K. Padmanabh, A. Malikarjuna, 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. In BuildSys, pages 37--42. ACM, 2009. Google ScholarDigital Library
- K. W. Roth, D. Westphalen, P. Llana, and M. Feng. The energy impact of faults in U.S. commercial buildings. In Int'l Refrigeration and Air Conditioning Conference, 2004.Google Scholar
- J. Taneja, A. Krioukov, S. Dawson-Haggerty, and D. Culler. Enabling advanced environmental conditioning with a building application stack. In International Green Computing Conference (IGCC), pages 1--10. IEEE, 2013.Google ScholarCross Ref
- U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy. Buildings energy data book, 2011.Google Scholar
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