Intra-Individual Differences in Predicting Personal Thermal Comfort Using Model-Based Recursive Partitioning (MOB)
Publication: Computing in Civil Engineering 2021
ABSTRACT
With the advent of personal comfort models in predicting thermal state, investigating individual differences, including gender, age, and race, has been widely conducted to predict personal thermal comfort. The personal comfort model is a good complement to the conventional predicted mean vote (PMV) model; however, the efforts to understand individual differences to date have been quite fragmented in terms of intra-individual differences. The underlying assumption of the current personal comfort models is that the generalized one personal model is universally applicable to any sub-personal variants. However, this assumption does not hold if intra-individual variant subgroups exist, and these subgroups differ in their thermal comfort state. Given that a person can have different thermal preferences from day-to-day and time-to-time under the same environment, interpreting one’s thermal comfort with one single model is not enough to understand intra-individual variances. To address such research gap and better understand human thermal comfort, this study aims to investigate underlying sub-personal thermal states with differential thermal comfort responses building upon the model-based (MOB) recursive partitioning model. To validate the proposed approach, building occupants’ thermal responses and their corresponding physiological data were collected through field experiments, and the performance was compared with current personal comfort modeling approach. It is worthwhile to revisit the overlooked intra-individual differences and explore appropriate modeling methods for better understanding of building occupants’ thermal comfort.
Get full access to this chapter
View all available purchase options and get full access to this chapter.
REFERENCES
Akimoto, T., Tanabe, S., Yanai, T., and Sasaki, M. (2010). “Thermal comfort and productivity-Evaluation of workplace environment in a task conditioned office.” Building and environment, Elsevier, 45(1), 45–50.
Allen, J. G., MacNaughton, P., Laurent, J. G. C., Flanigan, S. S., Eitland, E. S., and Spengler, J. D. (2015). “Green buildings and health.” Current Environmental Health Reports, Springer, 2(3), 250–258.
ANSI/ASHRAE. (2013). ASHRAE Standard 55: Thermal Environmental Conditions for Human Occupancy. American Society of Heating, Refrigerating and Air- Conditioning Engineers Inc.
Cheng, X., Yang, B., Hedman, A., Olofsson, T., Li, H., and Van Gool, L. (2018). “Non-invasive thermal comfort perception based on subtleness magnification and deep learning for energy efficiency.”
Choi, D., and Zeng, L. (2020). “Robust logistic regression tree for subgroup identification in healthcare outcome modeling.” IISE Transactions on Healthcare Systems Engineering, Informa UK Limited, 1–16.
Fanger, P. O. (1970). “Thermal comfort. Analysis and applications in environmental engineering.” Thermal comfort. Analysis and applications in environmental engineering., Copenhagen: Danish Technical Press.
Fountain, M., Brager, G., and De Dear, R. (1996). “Expectations of indoor climate control.” Energy and Buildings, Elsevier, 24(3), 179–182.
Humphreys, M. A., and Fergus Nicol, J. (2002). “The validity of ISO-PMV for predicting comfort votes in every-day thermal environments.” Energy and Buildings, Elsevier, 34(6), 667–684.
ISO. (2005). 7730: 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 criteria. International Standard Organization, Geneva.
Jazizadeh, F., Ghahramani, A., Becerik-Gerber, B., Kichkaylo, T., and Orosz, M. (2014). “Human-Building Interaction Framework for Personalized Thermal Comfort-Driven Systems in Office Buildings.” Journal of Computing in Civil Engineering, 28(1), 2–16.
Kim, J., Zhou, Y., Schiavon, S., Raftery, P., and Brager, G. (2018). “Personal comfort models: predicting individuals’ thermal preference using occupant heating and cooling behavior and machine learning.” Building and Environment, Elsevier, 129, 96–106.
Kopf, J., Augustin, T., and Strobl, C. (2013). “The potential of model-based recursive partitioning in the social sciences: Revisiting Ockham’s Razor.” Contemporary Issues in Exploratory Data Mining in the Behavioral Sciences, (88), 97–117.
Lee, J., and Ham, Y. (2020). “Physiological sensing-driven personal thermal comfort modelling in consideration of human activity variations.” Building Research and Information, Routledge.
Lee, J., Ham, Y., and Suermann, P. (2020). “Investigating the effect of reflecting human activity information in physiological sensing-driven occupant thermal comfort modeling.” Construction Research Congress 2020: Infrastructure Systems and Sustainability - Selected Papers from the Construction Research Congress 2020, American Society of Civil Engineers (ASCE), 139–147.
Li, D., Menassa, C. C., and Kamat, V. R. (2017). “Personalized human comfort in indoor building environments under diverse conditioning modes.” Building and Environment, 126, 304–317.
Liu, S., Schiavon, S., Das, H. P., Jin, M., and Spanos, C. J. (2019). “Personal thermal comfort models with wearable sensors.” Building and Environment, 162, 106281.
Loh, W.-Y., Cao, L., and Zhou, P. (2019). “Subgroup identification for precision medicine: A comparative review of 13 methods.”
Rupp, R. F., Vásquez, N. G., and Lamberts, R. (2015). “A review of human thermal comfort in the built environment.” Energy and Buildings, Elsevier, 105, 178–205.
Rusch, T. (2013). “Gaining Insight with Recursive Partitioning of Generalized Linear Models.” 83(LeCam 1990), 1301–1315.
Samet, J. M., and Spengler, J. D. (2003). “Indoor environments and health: moving into the 21st century.” American Journal of Public Health, American Public Health Association, 93(9), 1489–1493.
Seibold, H., Zeileis, A., and Hothorn, T. (2016). “Model-Based Recursive Partitioning for Subgroup Analyses.” International Journal of Biostatistics, 12(1), 45–63.
Tanabe, S., Iwahashi, Y., Tsushima, S., and Nishihara, N. (2013). “Thermal comfort and productivity in offices under mandatory electricity savings after the Great East Japan earthquake.” Architectural Science Review, Taylor & Francis, 56(1), 4–13.
Wang, Z., de Dear, R., Luo, M., Lin, B., He, Y., Ghahramani, A., and Zhu, Y. (2018). “Individual difference in thermal comfort: A literature review.” Building and Environment, Pergamon, 138, 181–193.
Wyon, D. P. (2004). “The effects of indoor air quality on performance and productivity.” Indoor air, 14, 92–101.
Yang, B., Cheng, X., Dai, D., Olofsson, T., Li, H., and Meier, A. (2019). “Real-time and contactless measurements of thermal discomfort based on human poses for energy efficient control of buildings.” Building and Environment, Elsevier Ltd, 162, 106284.
Zeileis, A., Hothorn, T., and Hornik, K. (2008). “Model-based recursive partitioning.” Journal of Computational and Graphical Statistics, 17(2), 492–514.
Zhao, Q., Zhao, Y., Wang, F., Wang, J., Jiang, Y., and Zhang, F. (2014). “A data-driven method to describe the personalized dynamic thermal comfort in ordinary office environment: From model to application.” Building and Environment, Elsevier, 72, 309–318.
Information & Authors
Information
Published In
Computing in Civil Engineering 2021
Pages: 1212 - 1219
History
Published online: May 24, 2022
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.