Investigating Occupancy-Driven Air-Conditioning Control Based on Thermal Comfort Level
Publication: Journal of Architectural Engineering
Volume 24, Issue 2
Abstract
Current air-conditioning systems often rely on maximum occupancy assumptions and fixed schedules to maintain a sufficient comfort level. Having knowledge regarding the occupancy situation may lead to significant energy savings in a building. Therefore, the paper proposes a method to investigate an occupancy-driven HVAC control system that is based on thermal comfort analysis. Computational fluid dynamics (CFD) was used to evaluate thermal comfort through modeling of the indoor air distribution and flow. Air velocity and temperature were simulated in several scenarios and the predicted mean vote (PMV) and the predicted percentage dissatisfied (PPD) were computed. The simulation results were verified through a survey asking for occupants’ feelings, and the consequential thermal comfort profiles were identified, which were used for creating possible energy savings. Moreover, a predefined working schedule and the historical behavior of persons were used to develop a pattern for predicting personal occupancy situations. Finally, all variables were imported into an intelligence system to fulfill intelligent control of the air-conditioning system. The results show good potential to reduce energy consumption while meeting the comfort requirements of occupants.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
The authors acknowledge financial support of the Research Institute for Smart and Green Cities (RISGC) at Xi'an Jiaotong–Liverpool University in China (Project RISGC-2017-01). This work was also made possible by a grant for the postgraduate research studentship from the Department of Civil Engineering at Xi'an Jiaotong–Liverpool University.
References
Airpak [Computer software]. ANSYS, Canonsburg, PA.
ASHRAE (American Society of Heating, Refrigeration and Air-Conditioning Engineers). (2013). “Thermal environmental conditions for human occupancy.” ANSI/ASHRAE 55-2013, Atlanta.
Brager, G. S., and de Dear, R. J. (1998). “Thermal adaptation in the built environment: A literature review.” Energy Build., 27(1), 83–96.
Campion, N., Thiel, C. L., Focareta, J., and Bilec, M. M. (2016). “Understanding green building design and healthcare outcomes: Evidence-based design analysis of an oncology unit.” J. Archit. Eng., 04016009.
Choi, J. H. (2010). “CoBi: Bio-sensing building mechanical system controls for sustainably enhancing individual thermal comfort.” Ph.D. dissertation, Carnegie Mellon Univ., Pittsburgh.
Ciabattoni, L., Cimini, G., Ferracuti, F., Grisostomi, M., Ippoliti, G., and Pirro, M. (2015). “Indoor thermal comfort control through fuzzy logic PMV optimization.” Int. Joint Conf., Neural Networks (IJCNN), IEEE, New York.
Daum, D., Haldi, F., and Morel, N. (2011). “A personalized measure of thermal comfort for building controls.” Build. Environ., 46(1), 3–11.
Erickson, V. L., Carreira-Perpiñán, M. Á., and Cerpa, A. E. (2011). “OBSERVE: Occupancy-based system for efficient reduction of HVAC energy.” Proc., 10th ACM/IEEE Int. Conf., Information Processing in Sensor Networks, IEEE, New York, 258–269.
Erickson, V. L., and Cerpa, A. (2012). “Thermovote: Participatory sensing for efficient building HVAC conditioning.” Proc., BuildSys '12, 4th ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings, Association for Computer Machinery, New York, 9–16.
European Union. (2016). “Energy efficiency of buildings: A nearly zero-energy future?” 〈http://www.europarl.europa.eu/RegData/etudes/BRIE/2016/582022/EPRS_BRI%282016%29582022_EN.pdf〉 (Dec. 20, 2016).
Fanger, P. O. (1970). Thermal comfort. Analysis and application in environmental engineering, Danish Technical Press, Copenhagen, Denmark.
Feldmeier, M., and Paradiso, J. A. (2010). “Personalized HVAC control system.” Proc., 2010 Internet of Things Conf., IEEE, New York.
Ferreira, P. M., Ruano, A., Silva, S., and Conceição, E. Z. E. (2012). “Neural networks based predictive control for thermal comfort and energy savings in public buildings.” Energy Build., 55(Dec), 238–251.
Freire, R. Z., Oliveira, G. H. C., and Mendes, N. (2008). “Predictive controllers for thermal comfort optimization and energy savings.” Energy Build., 40(7), 1353–1365.
Guo, W., and Zhou, M. (2009). “Technologies toward thermal comfort-based and energy-efficient HVAC systems: A review.” Proc., 2009 IEEE Int. Conf. on Systems, Man, and Cybernetics, IEEE, New York, 3883–3888.
ISO. (2005). “Ergonomics of the thermal environment—Analytical determination and interpretation of thermal comfort using calculation of the PMV and PPD indices and local thermal comfort.” EN ISO 7730, Geneva.
Jazizadeh, F., Ghahramani, A., Becerik-Gerber, B., Kichkaylo, T., and Orosz, M. (2014). “User-led decentralized thermal comfort driven HVAC operations for improved efficiency in office buildings.” Energy Build., 70(Feb), 398–410.
Kan, E. M., Yadanar, K., Ling, N. H., Soh, Y., and Lin, N. (2014). “Multi-agent control system with intelligent optimization for building energy management.” Proc., 18th Asia Pacific Symp., Intelligent and Evolutionary Systems, Vol. 2, H. Handa, H. Ishibuchi, Y.-S. Ong, and K. C. Tan, eds., Springer International, Cham, Switzerland, 505–518.
Kaviany, M. (2002). Principles of heat transfer, John Wiley & Sons, New York.
Klein, L., et al. (2012). “Coordinating occupant behavior for building energy and comfort management using multi-agent system.” Autom. Constr., 22(Mar), 525–536.
Lajoie, P., et al. (2015). “The IVAIRE project—A randomized controlled study of the impact of ventilation on indoor air quality and the respiratory symptoms of asthmatic children in single family homes.” Indoor Air, 25(6), 582–597.
Lee, Y. S., and Malkawi, A. (2014). “Simulating multiple occupant behaviors in buildings: An agent-based modeling approach.” Energy Build., 69(Feb), 407–416.
Magdalena, H., Geron, M., and Keane, M. M. (2013). “Calibrated CFD simulation to evaluate thermal comfort in a highly-glazed naturally ventilated room.” Build. Environ., 70(Dec), 73–89.
Marshall, E., Steinberger, J., Foxon, T., and Dupont, V. (2016). “Modelling the delivery of residential thermal comfort and energy savings: comparing how occupancy type affects the success of energy efficiency measures.” Proc., Int. Conf., Sustainable Ecological Engineering Design for Society (SEEDS), Sustainable Ecological Engineering Design, M. Dastbaz and C. Gorse, eds., Springer International, Cham, Switzerland, 327–339.
Meyn, S. P., and Tweedie, R. L. (2009). Markov chains and stochastic stability, 2nd Ed., Cambridge University Press, Cambridge, U.K.
Moshfegh, B., and Nyiredy, R. (2004). “Comparing RANS models for flow and thermal analysis of pin fin heat sinks.” Proc., 15th Australasian Fluid Mechanics Conf., M. Behnia, W. Lin, and G. D. McBain, eds., Univ. of Sydney, Sydney, Australia.
Murakami, Y., Terano, M., Mizutani, K., Harada, M., and Kuno, S. (2007). “Field experiments on energy consumption and thermal comfort in the office environment controlled by occupants’ requirements from PC terminal.” Build. Environ., 42(12), 4022–4027.
Nasrollahi, N., Knight, I., and Jones, P. (2008). “Workplace satisfaction and thermal comfort in air conditioned office buildings: Findings from a summer survey and field experiments in Iran.” Indoor Built Environ., 17(1), 69–79.
Nassif, N., Kajl, S., and Sabourin, R. (2004). “Two-objective on-line optimization of supervisory control strategy.” Build. Serv. Eng. Res. Technol., 25(3), 241–251.
Nassif, N., and Moujaes, S. (2008). “A cost-effective operating strategy to reduce energy consumption in a HVAC system.” Int. J. Energy Res., 32(6), 543–558.
Nicol, J. (2008). A handbook of adaptive thermal comfort: Towards a dynamic model low energy architecture research unit, London Metropolitan Univ., London.
Nowak, M., and Urbaniak, A. (2011). “Utilization of intelligent control algorithms for thermal comfort optimization and energy saving.” Proc., 12th Int. Carpathian Control Conf. (ICCC), IEEE, New York.
Papadopoulos, S., and Azar, E. (2016). “Optimizing HVAC operation in commercial buildings: A genetic algorithm multi-objective optimization framework.” Proc., Winter Simulation Conf. (WSC), IEEE, New York.
Pazhoohesh, M. (2017). “Occupancy-driven intelligent control of HVAC based on thermal comfort.” Ph.D. dissertation, Univ. of Liverpool, Liverpool, U.K.
Pazhoohesh, M., Shahmir, R., and Zhang, C. (2015). “Investigating thermal comfort and occupants position impacts on building sustainability using CFD and BIM.” Proc., Living and Learning: 49th Int. Conf., Architectural Science Association: Research for a Better Built Environment, R. H. Crawford and A. Stephan, eds., Architectural Science Association, Sydney, Australia.
Pazhoohesh, M., and Zhang, C. (2015). “A practical localization system based on principle component analysis.” Proc., 2nd World Congress, Computer Applications and Information Systems, N&N Global Technology, Tozeur, Tunisia.
Songuppakarn, T., Wongsuwan, W., and San-um, W. (2014). “Artificial neural networks based prediction for thermal comfort in an academic classroom.” Proc., Int. Conf. and Utility Exhibition on Green Energy for Sustainable Development (ICUE), IEEE, New York.
Wagner, A., Gossauer, E., Moosmann, C., Gropp, T., and Leonhart, R. (2007). “Thermal comfort and workplace occupant satisfaction—Results of field studies in German low energy office buildings.” Energy Build., 39(7), 758–769.
Wang, L. (2003). “The WM method completed: A flexible fuzzy system approach to data mining.” IEEE Trans. Fuzzy Syst., 11(6), 768–782.
Wang, L-X., and Mendel, J. M. (1991). “Generating fuzzy rules by learning from examples.” IEEE Trans. Syst., Man, Cybern., 22(6), 1414–1427.
Yang, L., Ye, M., and He, B. J. (2014). “CFD simulation research on residential indoor air quality.” Sci. Total Environ., 472, 1137–1144.
Yu, T.-C., and Lin, C.-C. (2015). “An intelligent wireless sensing and control system to improve indoor air quality: Monitoring, prediction, and preaction.” Int. J. Distrib. Sens. Netw., 11(8).
Information & Authors
Information
Published In
Journal of Architectural Engineering
Volume 24 • Issue 2 • June 2018
Copyright
© 2018 American Society of Civil Engineers.
History
Received: Dec 20, 2016
Accepted: Sep 11, 2017
Published online: Jan 17, 2018
Published in print: Jun 1, 2018
Discussion open until: Jun 17, 2018
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.