Abstract
We are in an age where people are paying increasing attention to energy conservation around the world. The heating and air-conditioning systems of buildings introduce one of the largest chunks of energy expenses. In this article, we make a key observation that after a meeting or a class ends in a room, the indoor temperature will not immediately increase to the outdoor temperature. We call this phenomenon thermal inertia. Thus, if we arrange subsequent meetings in the same room rather than in a room that has not been used for some time, we can take advantage of such undissipated cool or heated air and conserve energy. Though many existing energy conservation solutions for buildings can intelligently turn off facilities when people are absent, we believe that understanding thermal inertia can lead system designs to go beyond on-and-off-based solutions to a wider realm.
We propose a framework for exploring thermal inertia in room management. Our framework contains two components. (1) The energy-temperature correlation model captures the relation between indoor temperature change and energy consumption. (2) The energy-aware scheduling algorithms: given information for the relation between energy and temperature change, energy-aware scheduling algorithms arrange meetings not only based on common restrictions, such as meeting time and room capacity requirement, but also energy consumptions. We identify the interface between these components so further works towards same on direction can make efforts on individual components.
We develop a system to verify our framework. First, it has a wireless sensor network to collect indoor, outdoor temperature and electricity expenses of the heating or air-conditioning devices. Second, we build an energy-temperature correlation model for the energy expenses and the corresponding room temperature. Third, we develop room scheduling algorithms. In detail, we first extend the current sensor hardware so that it can record the electricity expenses in re-heating or re-cooling a room. As the sensor network needs to work unattendedly, we develop a hardware board for long-range communications so that the Imote2 can send data to a remote server without a computer relay close by. An efficient two-tiered sensor network is developed with our extended Imote2 and TelosB sensors. We apply laws of thermodynamics and build a correlation model of the energy needed to re-cool a room to a target temperature. Such model requires parameter calibration and uses the data collected from the sensor network for model refinement. Armed with the energy-temperature correlation model, we develop an optimal algorithm for a specified case, and we further develop two fast heuristics for different practical scenarios.
Our demo system is validated with real deployment of a sensor network for data collection and thermodynamics model calibration. We conduct a comprehensive evaluation with synthetic room and meeting configurations, as well as real class schedules and classroom topologies of The Hong Kong Polytechnic University, academic calendar year of Spring 2011. We observe 20% energy savings as compared with the current schedules.
- Agarwal, Y., Balaji, B., Dutta, S., Gupta, R., and Weng, T. 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.Google Scholar
- Agarwal, Y., Balaji, B., Gupta, R., Lyles, J., Wei, M., and Weng, T. 2010. Occupancy-driven energy management for smart building automation. In Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings (BuildSys'10). ACM. Google ScholarDigital Library
- Arkin, E. and Silverberg, E. 1987. Scheduling jobs with fixed start and end times. Discrete Appl. Math. 18, 1, 1--8. Google ScholarDigital Library
- Aswani, A., Master, N., Taneja, J., Culler, D., and Tomlin., C. 2012. Reducing transient and steady state electricity consumption in HVAC using learning-based model predictive control. Proc. IEEE 100, 1, 240--253.Google ScholarCross Ref
- Burke, E. K. and Petrovic, S. 2002. Recent research directions in automated timetabling. Euro. J. Oper. Res. 140, 2, 266--280.Google ScholarCross Ref
- Carter, M. 2001. A comprehensive course timetabling and student scheduling system at the University of Waterloo. In Practice and Theory of Automated Timetabling III, E. Burke and W. Erben, Eds., Lecture Notes in Computer Science, vol. 2079, Springer, Berlin, 64--82. Google ScholarDigital Library
- Chantrasrisalai, C., Ghatti, V., Fisher, D., and Scheatzle, D. 2003. Experimental validation of the energyplus low-temperature radiant simulation. ASHRAE Trans. 109, 2, 614--623.Google Scholar
- Chun, A., Wai, H., and Wong, R. 2003. Optimizing agent-based meeting scheduling through preference estimation. Eng. Appl. Artif. Intell. 16, 7C8, 727--743.Google Scholar
- Dawson-Haggerty, S., Jiang, X., Tolle, G., Ortiz, J., and Culler, D. 2010. SMAP - a simple measurement and actuation profile for physical information. In Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems (Sensys'10). ACM. Google ScholarDigital Library
- Deng, K., Barooah, P., Mehta, P. G., and Meyn, S. P. 2010. Building thermal model reduction via aggregation of states. In Proceedings of the American Control Conference (ACC). 5118--5123.Google Scholar
- DOE. 2010. Getting started with energyplus. Tech. rep., U.S. Department of Energy. http://apps1.eere.energy.gov/buildings/energyplus/pdfs/gettingstarted.pdf.Google Scholar
- Ellis, C., Scott, J., Hazas, M., and Krumm, J. C. 2012. Earlyoff: Using house cooling rates to save energy. In Proceedings of the 4nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings (BuildSys'12). ACM. Google ScholarDigital Library
- Elmohamed, M., Coddington, P., and Fox, G. 1998. A comparison of annealing techniques for academic course scheduling. In Practice and Theory of Automated Timetabling II, E. Burke and M. Carter, Eds., Lecture Notes in Computer Science, vol. 1408, Springer, Berlin, 92--112. 10.1007/BFb0055883. Google ScholarDigital Library
- EMSD. 2010. Hong Kong energy end-use data. Tech. rep., Electrical and Mechanical Service Department (EMSD), Hong Kong. http://www.emsd.gov.hk/emsd/e_download/pee/HKEEUD2010.pdf.Google Scholar
- Erickson, V., Carreira-Perpiñán, M., and Cerpa, A. 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.Google Scholar
- Erickson, V. and Cerpa, A. E. 2012. Tempvote: Participatory sensing for efficient building HVAC conditioning. In Proceedings of the 4nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings (BuildSys'12). ACM. Google ScholarDigital Library
- Erickson, V., Lin, Y., Kamthe, A., Brahme, R., Surana, A., Cerpa, A., Sohn, M., and Narayanan, S. 2010. Energy efficient building environment control strategies using real-time occupancy measurements. In Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings (BuildSys'10). ACM. Google ScholarDigital Library
- Gnawali, O., Fonseca, R., Jamieson, K., Moss, D., and Levis, P. 2009. Collection tree protocol. In Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems (Sensys'09). ACM. Google ScholarDigital Library
- Goswami, J. and Chan, A. 2011. Fundamentals of Wavelets: Theory, Algorithms, and Applications. Wiley Press. Google ScholarDigital Library
- He, M., Cai, W., and Li, S. 2005. Multiple fuzzy model-based temperature predictive control for HVAC systems. Info. Sci. 169, 1C2, 155--174. Google ScholarDigital Library
- Henze, G. P., Felsmann, C., and Knabe, G. 2004. Evaluation of optimal control for active and passive building thermal storage. Int. J. Thermal Sci. 43, 2, 173--183.Google ScholarCross Ref
- Hnat, T., Srinivasan, V., Lu, J., Sookoor, T., Dawson, R., Stankovic, J., and Whitehouse, K. 2011. The hitchhiker's guide to successful residential sensing deployments. In Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems (Sensys'11). ACM. Google ScholarDigital Library
- Jiang, X., Dawson-Haggerty, S., Dutta, P., and Culler, D. 2009a. Design and implementation of a high-fidelity AC metering network. In Proceedings of the 8th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN'09). ACM/IEEE. Google ScholarDigital Library
- Jiang, X., Ly, M., Taneja, J., Dutta, P., and Culer, D. 2009b. Experiences with a high-fidelity wireless building energy auditing network. In Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems (Sensys'09). ACM. Google ScholarDigital Library
- Lewis, R. 2008. A survey of metaheuristic-based techniques for university timetabling problems. OR Spectrum 30, 167--190. 10.1007/s00291-007-0097-0.Google ScholarCross Ref
- Li, T., Li, Q., Zhu, S., and Ogihara, M. 2002. A survey on wavelet applications in data mining. ACM SIGKDD Explo. Newslett. 2. Google ScholarDigital Library
- Liang, C., Liu, J., Luo, L., Terzis, A., and Zhao, F. 2009. Racnet: A high-fidelity data center sensing network. In Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems (Sensys'09). ACM. Google ScholarDigital Library
- Lienhard IV, J. H. and Lienhard V, J. H. 2003. A Heat Transfer Textbook 3rd Ed. Phlogiston Press.Google Scholar
- Lu, J., Birru, D., and Whitehouse, K. 2010a. Using simple light sensors to achieve smart daylight harvesting. In Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings (BuildSys'10). ACM. Google ScholarDigital Library
- Lu, J., Sookoor, T., Srinivasan, V., Gao, G., Holben, B., Stankovic, J., Field, E., and Whitehouse, K. 2010b. 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. Google ScholarDigital Library
- Majumdar, A., Albonesi, D., and Bose, P. 2012. Occupancy-driven energy management for smart building automation. In Proceedings of the 4nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings (BuildSys'12). ACM. Google ScholarDigital Library
- Murray, K., Mller, T., and Rudov, H. 2007. Modeling and solution of a complex university course timetabling problem. In Practice and Theory of Automated Timetabling VI, E. Burke and H. Rudov, Eds., Lecture Notes in Computer Science, vol. 3867, Springer, Berlin, 189--209. Google ScholarDigital Library
- Oldewurtel, F., Parisio, A., Jones, C., Morari, M., Gyalistras, D., Gwerder, M., Stauch, V., Lehmann, B., and Wirth, K. 2010. Energy efficient building climate control using stochastic model predictive control and weather predictions. In Proceedings of the American Control Conference (ACC). 5100--5105.Google Scholar
- Padmanabh, K., Malikarjuna, A., Sen, S., Katru, S., Kumar, A., Pawankumar, S., Vuppala, S., and Paul, S. 2009. Isense: A wireless sensor network based conference room management system. In Proceedings of the 1st ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings (BuildSys'09). ACM. Google ScholarDigital Library
- Raghavendra, R., Ranganathan, P., Talwar, V., Wang, Z., and Zhu, X. 2008. No power struggles: Coordinated multi-level power management for the data center. In Proceedings of the 13th ACM Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS'08). ACM. Google ScholarDigital Library
- Sauer, H., Howell, R., and Coad, W. 2001. Principles of Heating, Ventilating, and Air Conditioning. American Society of Heating.Google Scholar
- Schell, M. and Inthout, D. 2001. Demand control ventilation using CO2. ASHRAE J. 42, 2, 18--29.Google Scholar
- Schor, L., Sommer, P., and Wattenhofer, R. 2009. Towards a zero-configuration wireless sensor network architecture for smart buildings. In Proceedings of the 1st ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings (BuildSys'09). ACM. Google ScholarDigital Library
- Shang, Y., Li, D., and Xu, M. 2010. Energy-aware routing in data center network. In Proceedings of the 1st ACM SIGCOMM Workshop on Green Networking (Green Networking'10). ACM. Google ScholarDigital Library
- Socha, K., Knowles, J., and Sampels, M. 2002. A max-min Ant system for the university course timetabling problem. In Ant Algorithms, M. Dorigo, G. Di Caro, and M. Sampels, Eds., Lecture Notes in Computer Science, vol. 2463, Springer, Berlin, 63--77. Google ScholarDigital Library
- Tashtoush, B., Molhim, M., and Al-Rousan, M. 2005. Dynamic model of an HVAC system for control analysis. Energy 30, 10, 1729--1745.Google ScholarCross Ref
- Tian, Z. and Love, J. A. 2009. Energy performance optimization of radiant slab cooling using building simulation and field measurements. Energy Build. 41, 3, 320--330.Google ScholarCross Ref
- Wang, S. and Jin, X. 1998. CO2-based occupancy detection for on-line outdoor air flow control. Indoor Built Environ. 7, 3, 165--181.Google ScholarCross Ref
- Wei, W. W. 2005. Time Series Analysis: Univariate and Multivariate Methods 2nd Ed. Addison Wesley.Google Scholar
- Wikipedia. 2010. Energy in the United States. http://en.wikipedia.org/wiki/Energy_in_the_United _States.Google Scholar
- Yuan, Y., Pan, D., Wang, D., Xu, X., Peng, Y., Peng, X., and Wan, P. 2011. Developing an energy conservation room management system using thermal inertia (matlab package). Tech. rep. http://www4.comp.polyu.edu.hk/∼csyiyuan/projects/ECRMS-TOSN/ECRMS.html.Google Scholar
- Zhang, M., Yi, C., Liu, B., and Zhang, B. 2010. Greente: Power-aware traffic engineering. In Proceedings of the 18th IEEE International Conference on Network Protocols (ICNP'10). Google ScholarDigital Library
- Zhou, Y., Wu, J., Wang, R., Shiochi, S., and Li, Y. 2008. Simulation and experimental validation of the variable-refrigerant-volume (VRV) air-conditioning system in energyplus. Energy Build. 40, 6, 1041--1047.Google ScholarCross Ref
Index Terms
- A study towards applying thermal inertia for energy conservation in rooms
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