Improving Occupant Thermal Comfort through Personalized Space Recommendation
Publication: Journal of Computing in Civil Engineering
Volume 37, Issue 1
Abstract
Thermal comfort significantly affects occupants’ satisfaction, well-being, and productivity in built environments. To improve thermal comfort, existing literature has investigated human-in-the-loop HVAC control and personal comfort systems (PCS) to adjust the macro- and microenvironments surrounding occupants to meet their preferences. However, these methods have limitations, including the inability to satisfy all occupants, energy waste due to undesired fluctuations in HVAC settings, uncertainty in energy savings, high upfront costs, lack of scalability, to name a few. In contrast, this study proposed a SpaceMatch framework by reimagining occupants as mobile agents who are willing to move to spaces where the conditions best match their personal preferences and needs. This framework leverages personal comfort models developed for each occupant using machine learning and natural spatial-temporal temperature variations in buildings to make space recommendations. An experiment with 12 subjects was conducted in a testbed building from October to November 2021 at Clemson University to validate the proposed framework. The results showed that subjects’ thermal comfort increased at least by 18.8% with the help of space recommendations compared to the baseline, without incurring additional energy use. This framework has great potential in many built environments where flexible workplace strategies are employed, such as open-plan offices and libraries, especially in the postpandemic era when people’s working habits have significantly changed because of remote work, job autonomy, and flexible scheduling.
Practical Applications
The SpaceMatch framework proposed in this study is expected to fundamentally transform the way buildings are operated in a more flexible, comfortable, and sustainable direction that better supports their occupants’ thermal preferences and needs. The proposed framework aims to leverage buildings’ natural spatial-temporal temperature variations that result from their layouts, occupancy levels, the time of day, and so on, or synthetic variations from thermostat setbacks to accommodate each occupant. The goal is to reduce the response time, conflicts in individual preferences, and energy use. This new concept embraces the future workspaces of the postpandemic era, when increased job autonomy, flexible work schedules and locations, and coworking spaces will become the “new normal,” driving facilities managers to rethink their operations strategies to promote comfort, equity, and social justice for indoor environments among occupants of different genders, ages, cultures, and ethnicities.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
All the data, models, or code that support the findings of this study can be made available from the corresponding author upon reasonable request.
Acknowledgments
The authors thank Yongjian Zhao, Matt Callicott, Tim Howard (facility manager for the Watt Family Innovation Center), and Sethunya Mokoko (assistant director at the Clemson writing center) for their help and support, and all subjects who participated in this research.
References
Aryal, A., and B. Becerik-Gerber. 2019. “A comparative study of predicting individual thermal sensation and satisfaction using wrist-worn temperature sensor, thermal camera and ambient temperature sensor.” Build. Environ. 160 (Aug): 106223. https://doi.org/10.1016/j.buildenv.2019.106223.
Aryal, A., B. Becerik-Gerber, F. Anselmo, S. C. Roll, and G. M. Lucas. 2019. “Smart desks to promote comfort, health, and productivity in offices: A vision for future workplaces.” Front. Build. Environ. 5 (76): 1–14. https://doi.org/10.3389/fbuil.2019.00076.
ASHRAE (American Society of Heating, Refrigerating and Air-Conditioning Engineers). 2004. Thermal environmental conditions for human occupancy. Atlanta: ASHRAE.
ASHRAE (American Society of Heating, Refrigerating and Air-Conditioning Engineers). 2017. Standard 55–2017 thermal environmental conditions for human occupancy. Atlanta: ASHRAE.
Berelson, K., F. Simini, T. Tryfonas, and P. Cooper. 2018. “Sensor-based smart hot-desking for improvement of office well-being.” In Proc., 1st Int. Conf. on Digital Tools & Uses Congress, 1–9. New York: Association for Computing Machinery. https://doi.org/10.1145/3240117.3240131.
Candido, C., L. Thomas, S. Haddad, F. Zhang, M. Mackey, and W. Ye. 2019. “Designing activity-based workspaces: Satisfaction, productivity and physical activity.” Build. Res. Inf. 47 (3): 275–289. https://doi.org/10.1080/09613218.2018.1476372.
Capozzoli, A., M. S. Piscitelli, A. Gorrino, I. Ballarini, and V. Corrado. 2017. “Data analytics for occupancy pattern learning to reduce the energy consumption of HVAC systems in office buildings.” Sustain. Cities Soc. 35 (Nov): 191–208. https://doi.org/10.1016/j.scs.2017.07.016.
Chai, Q., H. Wang, Y. Zhai, and L. Yang. 2020. “Using machine learning algorithms to predict occupants’ thermal comfort in naturally ventilated residential buildings.” Energy Build. 217 (Jun): 109937. https://doi.org/10.1016/j.enbuild.2020.109937.
Chaudhuri, T., Y. C. Soh, H. Li, and L. Xie. 2019. “A feedforward neural network based indoor-climate control framework for thermal comfort and energy saving in buildings.” Appl. Energy 248 (Aug): 44–53. https://doi.org/10.1016/j.apenergy.2019.04.065.
Cooper, P. B., K. Maraslis, T. Tryfonas, and G. Oikonomou. 2017. “An intelligent hot-desking model harnessing the power of occupancy sensing data.” Facilities 35 (13/14): 766–786. https://doi.org/10.1108/F-01-2016-0014.
Daum, D., F. Haldi, and N. Morel. 2011. “A personalized measure of thermal comfort for building controls.” Build. Environ. 46 (1): 3–11. https://doi.org/10.1016/j.buildenv.2010.06.011.
Dawal, S. Z. M., and Z. Taha. 2006. “The effect of job and environmental factors on job satisfaction in automotive industries.” Int. J. Occup. Saf. Ergon. 12 (3): 267–280. https://doi.org/10.1080/10803548.2006.11076687.
Deng, M., C. C. Menassa, and B. Fu. 2021. “Room match: Achieving thermal comfort through smart space allocation and environmental control in buildings.” In Proc., 2021 Winter Simulation Conf. (WSC), 1–11. New York: IEEE.
Deng, M., X. Wang, D. Li, and C. C. Menassa. 2022. “Digital ID framework for human-centric monitoring and control of smart buildings.” Building Simulation 15 (10): 1709–1728.
Frontczak, M., and P. Wargocki. 2011. “Literature survey on how different factors influence human comfort in indoor environments.” Build. Environ. 46 (4): 922–937. https://doi.org/10.1016/j.buildenv.2010.10.021.
Ghahramani, A., P. Galicia, D. Lehrer, Z. Varghese, Z. Wang, and Y. Pandit. 2020. “Artificial intelligence for efficient thermal comfort systems: Requirements, current applications and future directions.” Front. Built Environ. 6 (2020): 49. https://doi.org/10.3389/fbuil.2020.00049.
Ghahramani, A., C. Tang, and B. Becerik-Gerber. 2015. “An online learning approach for quantifying personalized thermal comfort via adaptive stochastic modeling.” Build. Environ. 92 (Oct): 86–96. https://doi.org/10.1016/j.buildenv.2015.04.017.
Hilliard, T., L. Swan, and Z. Qin. 2017. “Experimental implementation of whole building MPC with zone based thermal comfort adjustments.” Build. Environ. 125 (Nov): 326–338. https://doi.org/10.1016/j.buildenv.2017.09.003.
Jazizadeh, F., A. Ghahramani, B. Becerikgerber, T. Kichkaylo, and M. Orosz. 2014. “Human-building interaction framework for personalized thermal comfort-driven systems in office buildings.” J. Comput. Civ. Eng. 28 (1): 2–16. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000300.
Jazizadeh, F., and W. Jung. 2018. “Personalized thermal comfort inference using RGB video images for distributed HVAC control.” Appl. Energy 220 (Jun): 829–841. https://doi.org/10.1016/j.apenergy.2018.02.049.
Jung, W., and F. Jazizadeh. 2019. “Human-in-the-loop HVAC operations: A quantitative review on occupancy, comfort, and energy-efficiency dimensions.” Appl. Energy 239: 1471–1508. https://doi.org/10.1016/j.apenergy.2019.01.070.
Jung, W., and F. Jazizadeh. 2020. “Energy saving potentials of integrating personal thermal comfort models for control of building systems: Comprehensive quantification through combinatorial consideration of influential parameters.” Appl. Energy 268 (Jun): 114882. https://doi.org/10.1016/j.apenergy.2020.114882.
Karmann, C., S. Schiavon, and E. Arens. 2018. “Percentage of commercial buildings showing at least 80% occupant satisfied with their thermal comfort.” In Proc., 10th Windsor Conf. Rethinking Comfort, 1–7. Berkeley, CA: Univ. of California.
Khovalyg, D., O. B. Kazanci, H. Halvorsen, I. Gundlach, W. P. Bahnfleth, J. Toftum, and B. W. Olesen. 2020. “Critical review of standards for indoor thermal environment and air quality.” Energy Build. 213 (Apr): 109819. https://doi.org/10.1016/j.enbuild.2020.109819.
Kim, J., F. Bauman, P. Raftery, E. Arens, H. Zhang, G. Fierro, M. P. Andersen, and D. Culler. 2019. “Occupant comfort and behavior: High-resolution data from a 6-month field study of personal comfort systems with 37 real office workers.” Build. Environ. 148 (Jan): 348–360. https://doi.org/10.1016/j.buildenv.2018.11.012.
Kim, J., Y. Zhou, S. Schiavon, P. Raftery, and G. Brager. 2018. “Personal comfort models: Predicting individuals’ thermal preference using occupant heating and cooling behavior and machine learning.” Build. Environ. 129 (1): 96–106.
Leccese, F., M. Rocca, G. Salvadori, E. Belloni, and C. Buratti. 2021. “Towards a holistic approach to indoor environmental quality assessment: Weighting schemes to combine effects of multiple environmental factors.” Energy Build. 245 (Aug): 111056. https://doi.org/10.1016/j.enbuild.2021.111056.
Lee, S., I. Bilionis, P. Karava, and A. Tzempelikos. 2017. “A Bayesian approach for probabilistic classification and inference of occupant thermal preferences in office buildings.” Build. Environ. 118 (Jun): 323–343. https://doi.org/10.1016/j.buildenv.2017.03.009.
Li, D., C. Menassa, and V. R. Kamat. 2019. “Feasibility of low-cost infrared thermal imaging to assess occupants thermal comfort.” In Proc., Int. Conf. on. Computing in Civil Engineering (i3CE): Smart Cities, Sustainability, and Resilience, 58–65. Reston, VA: ASCE.
Li, D., C. C. Menassa, and V. R. Kamat. 2017. “Personalized human comfort in indoor building environments under diverse conditioning modes.” Build. Environ. 126 (Dec): 304–317. https://doi.org/10.1016/j.buildenv.2017.10.004.
Li, D., C. C. Menassa, and V. R. Kamat. 2018. “Non-intrusive interpretation of human thermal comfort through analysis of facial infrared thermography.” Energy Build. 176 (Oct): 246–261. https://doi.org/10.1016/j.enbuild.2018.07.025.
Li, D., C. C. Menassa, and V. R. Kamat. 2019. “Robust non-intrusive interpretation of occupant thermal comfort in built environments with low-cost networked thermal cameras.” Appl. Energy 251 (Oct): 113336. https://doi.org/10.1016/j.apenergy.2019.113336.
Li, D., C. C. Menassa, V. R. Kamat, and E. Byon. 2020. “HEAT-human embodied autonomous thermostat.” Build. Environ. 178 (Jul): 106879. https://doi.org/10.1016/j.buildenv.2020.106879.
Lipczynska, A., S. Schiavon, and L. T. Graham. 2018. “Thermal comfort and self-reported productivity in an office with ceiling fans in the tropics.” Build. Environ. 135 (May): 202–212. https://doi.org/10.1016/j.buildenv.2018.03.013.
Luo, M., H. Zhang, E. Arens, A. Ghahramani, Z. Wang, L. Jin, and Y. He. 2018. “Heating and cooling the human body with energy-efficient personal comfort systems (PCS).” In Proc., 15th Conf. of Int. Society Indoor Air Quality & Climate (ISIAQ), 1–9. Berkeley, CA: Univ. of California.
McCartney, K., and M. Humphreys. 2002. “Thermal comfort and productivity.” In Vol 1 of Proc., Indoor Air 2002. 9th Int. Conf. on Indoor Air Quality and Climate, 822–827. Doetinchem, Netherlands: In-House.
Nagarathinam, S., A. Vasan, V. Sarangan, R. Jayaprakash, and A. Sivasubramaniam. 2021. “User placement and optimal cooling energy for co-working building spaces.” ACM Trans. Cyber-Phys. Syst. 5 (2): 1–24. https://doi.org/10.1145/3432818.
Pedregosa, F., G. Varoquaux, A. Gramfort, V. MichelB. Thirion, and O. Grisel. 2011. “Scikit-learn: Machine learning in Python.” J. Mach. Learn. Res. 12: 2825–2830.
Shahzad, S., J. K. Calautit, K. Calautit, B. Hughes, and A. I. Aquino. 2018. “Advanced personal comfort system (APCS) for the workplace: A review and case study.” Energy Build. 173 (Aug): 689–709. https://doi.org/10.1016/j.enbuild.2018.02.008.
Sood, T., P. Janssen, and C. Miller. 2020. “Spacematch: Using environmental preferences to match occupants to suitable activity-based workspaces.” Preprint submitted June 17, 2020. https://arxiv.org/abs/2006.09570.
Wang, C., F. Zhang, J. Wang, J. K. Doyle, P. A. Hancock, C. M. Mak, and S. Liu. 2021. “How indoor environmental quality affects occupants’ cognitive functions: A systematic review.” Build. Environ. 193 (Apr): 107647. https://doi.org/10.1016/j.buildenv.2021.107647.
Wang, X., D. Li, C. C. Menassa, and V. R. Kamat. 2019. “Investigating the effect of indoor thermal environment on occupants’ mental workload and task performance using electroencephalogram.” Build. Environ. 158 (Jul): 120–132. https://doi.org/10.1016/j.buildenv.2019.05.012.
Wei, P., S. Xia, R. Chen, J. Qian, C. Li, and X. Jiang. 2020. “A deep-reinforcement-learning-Based recommender system for occupant-driven energy optimization in commercial buildings.” IEEE Internet Things J. 7 (7): 6402–6413. https://doi.org/10.1109/JIOT.2020.2974848.
Wei, P., S. Xia, and X. Jiang. 2018. “Energy saving recommendations and user location modeling in commercial buildings.” In Proc., 26th Conf. on User Modeling, Adaptation and Personalization, 3–11. New York: Association for Computing Machinery. https://doi.org/10.1145/3209219.3209244.
Witterseh, T., D. P. Wyon, and G. Clausen. 2004. “The effects of moderate heat stress and open-plan office noise distraction on SBS symptoms and on the performance of office work.” Indoor Air 14 (s8): 30–40. https://doi.org/10.1111/j.1600-0668.2004.00305.x.
Xie, J., H. Li, C. Li, J. Zhang, and M. Luo. 2020. “Review on occupant-centric thermal comfort sensing, predicting, and controlling.” Energy Build. 226 (Nov): 110392. https://doi.org/10.1016/j.enbuild.2020.110392.
Xu, X., C.-F. Chen, D. Li, and C. Menassa. 2020. “Energy saving at work: Exploring the role of social norms, perceived control and ascribed responsibility in different office layouts.” Front. Built Environ. 6 (Feb): 1–12. https://doi.org/10.3389/fbuil.2020.00016.
Yan, B., E. Long, X. Meng, Y. Zhang, D. Hou, and X. Du. 2014. “Influence of user behavior on unsatisfactory indoor thermal environment.” Energy Convers. Manage. 86 (Oct): 1–7. https://doi.org/10.1016/j.enconman.2014.05.004.
Zhang, S., Y. Lu, and Z. Lin. 2020. “Coupled thermal comfort control of thermal condition profile of air distribution and thermal preferences.” Build. Environ. 177 (Jun): 106867. https://doi.org/10.1016/j.buildenv.2020.106867.
Zhao, Q., Y. Zhao, F. Wang, J. Wang, Y. Jiang, and F. Zhang. 2014. “A data-driven method to describe the personalized dynamic thermal comfort in ordinary office environment: From model to application.” Build. Environ. 72 (Feb): 309–318. https://doi.org/10.1016/j.buildenv.2013.11.008.
Zuo, C., L. Luo, and W. Liu. 2021. “Effects of increased humidity on physiological responses, thermal comfort, perceived air quality, and sick building syndrome symptoms at elevated indoor temperatures for subjects in a hot-humid climate.” Indoor Air 31 (2): 524–540. https://doi.org/10.1111/ina.12739.
Information & Authors
Information
Published In
Journal of Computing in Civil Engineering
Volume 37 • Issue 1 • January 2023
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Apr 25, 2022
Accepted: Sep 9, 2022
Published online: Nov 11, 2022
Published in print: Jan 1, 2023
Discussion open until: Apr 11, 2023
ASCE Technical Topics:
- Architectural engineering
- Artificial intelligence and machine learning
- Bibliographies
- Building systems
- Buildings
- Business management
- Computer programming
- Computing in civil engineering
- Continuum mechanics
- Deformation (mechanics)
- Energy methods
- Engineering fundamentals
- Engineering mechanics
- Human and behavioral factors
- HVAC
- Information management
- Practice and Profession
- Solid mechanics
- Structural engineering
- Structural mechanics
- Structures (by type)
- Thermal analysis
- Thermal effects
- Thermodynamics
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.
Cited by
- Shiqi Ding, Chengyu Tang, Snowil Lopes, Da Li, Assessing Energy Performance and Thermal Satisfaction of Flexible Space Usage in Office Buildings, Construction Research Congress 2024, 10.1061/9780784485279.033, (316-326), (2024).
- Yijin Zhao, Da Li, Multi-domain indoor environmental quality in buildings: A review of their interaction and combined effects on occupant satisfaction, Building and Environment, 10.1016/j.buildenv.2022.109844, 228, (109844), (2023).