ABSTRACT
Commercial buildings contribute to 19% of the primary energy consumption in the US, with HVAC systems accounting for 39.6% of this usage. To reduce HVAC energy use, prior studies have proposed using wireless occupancy sensors or even cameras for occupancy based actuation showing energy savings of up to 42%. However, most of these solutions require these sensors and the associated network to be designed, deployed, tested and maintained within existing buildings which is significantly costly.
We present Sentinel, a system that leverages existing WiFi infrastructure in commercial buildings along with smartphones with WiFi connectivity carried by building occupants to provide fine-grained occupancy based HVAC actuation. We have implemented Sentinel on top of RESTful web services, and demonstrate that it is scalable and compatible with legacy building management. We show that Sentinel accurately determines the occupancy in office spaces 86% of the time, with 6.2% false negative errors. We high-light the reasons for the inaccuracies, mostly attributed to aggressive power management by smartphones. Finally, we actuate 23% of the HVAC zones within a commercial building using Sentinel for one day and measure HVAC electrical energy savings of 17.8%.
- Aereco - Demand Controlled Ventilation. http://www.aereco.com/ventilation-systems/demand-controlled-ventilation, Mar. 2013.Google Scholar
- BACnet Stack. http://bacnet.sourceforge.net/, Mar. 2013.Google Scholar
- Enmetric Systems. http://www.enmetric.com/, Mar. 2013.Google Scholar
- FPL - Demand Controlled Ventilation. http://www.fpl.com/business/energy_saving/programs/interior/dcv.shtml, Mar. 2013.Google Scholar
- Philips Hue. https://www.meethue.com/, Mar. 2013.Google Scholar
- pyrad 2.0. https://pypi.python.org/pypi/pyrad, Mar. 2013.Google Scholar
- Y. Agarwal, B. Balaji, S. Dutta, R. Gupta, and T. Weng. Duty-Cycling Buildings Aggressively: The Next Frontier in HVAC Control. In Proc. of IEEE IPSN, 2011.Google Scholar
- Y. Agarwal, B. Balaji, R. Gupta, J. Lyles, M. Wei, and T. Weng. Occupancy-Driven Energy Management for Smart Building Automation. In Proc. of ACM BuildSys, 2010. Google ScholarDigital Library
- Y. Agarwal, R. Chandra, A. Wolman, P. Bahl, K. Chin, and R. Gupta. Wireless Wakeups Revisited: Energy management for VoIP over WiFi smartphones. In Proc. of ACM MobiSys, 2007. Google ScholarDigital Library
- Y. Agarwal, R. Gupta, D. Komaki, and T. Weng. BuildingDepot: An Extensible and Distributed Architecture for Building Data Storage, Access and Sharing. In Proc. of ACM BuildSys, 2012. Google ScholarDigital Library
- Y. Agarwal, T. Weng, and R. Gupta. The Energy Dashboard: Improving the Visibility of Energy Consumption at a Campus-Wide Scale. In Proc. of ACM BuildSys, 2009. Google ScholarDigital Library
- P. Arjunan, N. Batra, H. Choi, A. Singh, P. Singh, and M. B. Srivastava. SensorAct: A Privacy and Security Aware Federated Middleware for Building Management. In Proc. of ACM BuildSys, 2012. Google ScholarDigital Library
- A. Aswani, N. Master, J. Taneja, D. Culler, and C. Tomlin. Reducing Transient and Steady State Electricity Consumption in HVAC using Learning-Based Model-Predictive Control. Proc. of IEEE, 2012.Google ScholarCross Ref
- S. Aust, R. V. Prasad, and I. G. Niemegeers. IEEE 802.11 ah: Advantages in standards and further challenges for sub 1 GHz Wi-Fi. In Proc. of IEEE ICC, 2012.Google Scholar
- P. Bahl and V. N. Padmanabhan. RADAR: An In-Building RF-Based User Location and Tracking System. In Proc. of IEEE Infocom, 2000.Google ScholarCross Ref
- N. Balasubramanian, A. Balasubramanian, and A. Venkataramani. Energy consumption in mobile phones: a measurement study and implications for network applications. In Proc. of ACM SIGCOMM IMC, 2009. Google ScholarDigital Library
- M. J. Brandemuehl and J. E. Braun. The Impact of Demand-Controlled and Economizer Ventilation Strategies on Energy Use in Buildings. Technical report, Univ. of Colorado, Boulder, CO (US), 1999.Google Scholar
- S. T. Bushby. BACnet#8482;: A standard communication infrastructure for intelligent buildings. Automation in Construction, 1997.Google ScholarCross Ref
- A. Carroll and G. Heiser. An analysis of power consumption in a smartphone. In Proc. of USENIX ATC, 2010. Google ScholarDigital Library
- K. Chintalapudi, A. Padmanabha Iyer, and V. N. Padmanabhan. Indoor localization without the pain. In Proc. of ACM MobiCom, 2010. Google ScholarDigital Library
- J. Chung, M. Donahoe, C. Schmandt, I.-J. Kim, P. Razavai, and M. Wiseman. Indoor location sensing using geo-magnetism. In Proc. of ACM MobiSys, 2011. Google ScholarDigital Library
- D. B. Crawley, L. K. Lawrie, F. C. Winkelmann, W. F. Buhl, Y. J. Huang, C. O. Pedersen, R. K. Strand, R. J. Liesen, D. E. Fisher, M. J. Witte, et al. EnergyPlus: Creating a New-generation Building Energy Simulation Program. Energy and Buildings, 2001.Google ScholarCross Ref
- S. Dawson-Haggerty, X. Jiang, G. Tolle, J. Ortiz, and D. Culler. sMAP: A Simple Measurement and Actuation Profile for Physical Information. In Proc. of ACM SenSys, 2010. Google ScholarDigital Library
- S. Dawson-Haggerty, A. Krioukov, J. Taneja, S. Karandikar, G. Fierro, N. Kitaev, and D. Culler. BOSS: Building Operating System Services. In Proc. of USENIX NSDI, 2013. Google ScholarDigital Library
- S. Dawson-Haggerty, S. Lanzisera, J. Taneja, R. Brown, and D. Culler. @ scale: Insights from a Large, Long-Lived Appliance Energy WSN. In Proc. of IEEE IPSN, 2012. Google ScholarDigital Library
- S. Emmerich, J. Mitchell, and W. Beckman. Demand-Controlled Ventilation in a Multi-zone Office Building. Indoor and Built Environment, 1994.Google Scholar
- V. Erickson, S. Achleitner, and A. Cerpa. POEM: Power-Efficient Occupancy-Based Energy Management System. In Proc. of IEEE IPSN, 2013. Google ScholarDigital Library
- V. Erickson, M. Carreira-Perpiñán, and A. Cerpa. OBSERVE: Occupancy-Based System for Efficient Reduction of HVAC Energy. In Proc. of IEEE IPSN, 2011.Google Scholar
- W. J. Fisk and A. T. De Almeida. Sensor-Based Demand-Controlled Ventilation: A Review. Energy and buildings, 1998.Google Scholar
- W. J. Fisk, D. Faulkner, and D. Sullivan. A Pilot Study of the Accuracy of CO2 Sensors in Commercial Buildings. Lawrence Berkeley National Laboratory Paper LBNL E, 2008.Google Scholar
- S. K. Ghai, L. V. Thanayankizil, D. P. Seetharam, and D. Chakraborty. Occupancy Detection in Commercial Buildings using Opportunistic Context Sources. In In IEEE Percom Workshops, 2012.Google ScholarCross Ref
- S. Goyal, H. A. Ingley, and P. Barooah. Occupancy-based zone-climate control for energy-efficient buildings: Complexity vs. performance. Applied Energy, 2013.Google Scholar
- T. Hnat, V. Srinivasan, J. Lu, T. Sookoor, R. Dawson, J. Stankovic, and K. Whitehouse. The Hitchhiker's Guide to Successful Residential Sensing Deployments. In Proc. of ACM SenSys, 2011. Google ScholarDigital Library
- M. Hydeman. Advanced Variable Air Volume: System Design Guide: Design Guidelines. California Energy Commission, 2003.Google Scholar
- X. Jiang, S. Dawson-Haggerty, P. Dutta, and D. Culler. Design and implementation of a high-fidelity ac metering network. In Proc. of IEEE IPSN, 2009. Google ScholarDigital Library
- A. Krioukov, S. Dawson-Haggerty, L. Lee, O. Rehmane, and D. Culler. A Living Laboratory Study in Personalized Automated Lighting Controls. In Proc. of ACM BuildSys, 2011. Google ScholarDigital Library
- P. Lazik and A. Rowe. Indoor pseudo-ranging of mobile devices using ultrasonic chirps. In Proc. of ACM SenSys, 2012. Google ScholarDigital Library
- C. Martani, D. Lee, P. Robinson, R. Britter, and C. Ratti. ENERNET: Studying the Dynamic Relationship between Building Occupancy and Energy Consumption. Energy and Buildings, 2011.Google Scholar
- R. Melfi, B. Rosenblum, B. Nordman, and K. Christensen. Measuring Building Occupancy using Existing Network Infrastructure. In Proc. of IEEE IGCC, 2011. Google ScholarDigital Library
- F. Oldewurtel, A. Parisio, C. N. Jones, M. Morari, D. Gyalistras, M. Gwerder, V. Stauch, B. Lehmann, and K. Wirth. Energy Efficient Building Climate Control using Stochastic Model Predictive Control and Weather Predictions. In Proc. of IEEE ACC, 2010.Google ScholarCross Ref
- S. N. Patel, M. S. Reynolds, and G. D. Abowd. Detecting human movement by differential air pressure sensing in hvac system ductwork: An exploration in infrastructure mediated sensing. In Pervasive Computing. 2008. Google ScholarDigital Library
- S. N. Patel, K. N. Truong, and G. D. Abowd. Powerline positioning: A practical sub-room-level indoor location system for domestic use. In In Proc. of UbiComp. 2006. Google ScholarDigital Library
- A. Rahmati and L. Zhong. Context-for-wireless: context-sensitive energy-efficient wireless data transfer. In Proc. of ACM MobiSys, 2007. Google ScholarDigital Library
- A. Rowe, M. Berges, G. Bhatia, E. Goldman, R. Rajkumar, J. Garrett, J. Moura, and L. Soibelman. Sensor Andrew: Large-Scale Campus-Wide Sensing and Actuation. IBM Journal of Research and Development, 2011. Google ScholarDigital Library
- J. Taneja, A. Krioukov, S. Dawson-Haggerty, and D. E. Culler. Enabling Advanced Environmental Conditioning with a Building Application Stack. 2013.Google Scholar
- L. Thanayankizil, S. Ghai, D. Chakraborty, and D. Seetharam. Softgreen: Towards Energy Management of Green Office Buildings with Soft Sensors. In In Proc. of IEEE COMSNETS, 2012.Google Scholar
- D. Turner, S. Savage, and A. C. Snoeren. On the Empirical Performance of Self-Calibrating Wifi Location Systems. In Prof. of IEEE LCN, 2011. Google ScholarDigital Library
- US Department of Energy. Buildings Energy Data Book. http://buildingsdatabook.eren.doe.gov/, Aug. 2012.Google Scholar
- H. Wang, S. Sen, A. Elgohary, M. Farid, M. Youssef, and R. R. Choudhury. No need to war-drive: Unsupervised indoor localization. In Proc. of ACM MobiSys, 2012. Google ScholarDigital Library
- M. Youssef and A. Agrawala. The horus wlan location determination system. In Proc. of ACM MobiSys, 2005. Google ScholarDigital Library
- L. Zhang, B. Tiwana, Z. Qian, Z. Wang, R. P. Dick, Z. M. Mao, and L. Yang. Accurate online power estimation and automatic battery behavior based power model generation for smartphones. In Proc. of ACM CODES+ISSS, 2010. Google ScholarDigital Library
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