Non-Intrusive Method for Capturing Occupant Thermal Discomfort Cues and Profiles in Buildings
Publication: Construction Research Congress 2022
ABSTRACT
Most building energy-saving strategies are based on thermal comfort standards defined by regulatory bodies (e.g., ASHRAE), which do not account for individual differences or preferences of the building occupants. To address this gap, this paper proposes a non-intrusive automated method to capture the individual thermal discomfort states of occupants over time based on their thermal discomfort cues. The method aims to support the identification of personalized energy-saving strategies toward improved occupant thermal comfort and reduced building energy consumption. It uses a deep learning model to recognize occupant cues from videos. To implement and test the proposed approach, participants were video recorded in uncontrolled office settings while performing work-related activities. The deep learning model was used to recognize the thermal discomfort cues, and a thermal comfort profile that captures the discomfort states of occupants over time was developed. The method achieved a weighted recall and precision of 84% and 96%, respectively, in recognizing building occupant thermal discomfort cues.
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REFERENCES
Abhinandana, B., Beddiar, K., Benamour, M., Amirat, Y., and Benbouzid, M. (2018). “Intelligent Systems for Building Energy and Occupant Comfort Optimization: A State of the Art Review and Recommendations”. Energies.
Chaudhuri, T., Zhai, D., Soh, Y. C., Li, H., and Xie, L. (2018). “Thermal comfort prediction using normalized skin temperature in a uniform built environment”. Building and Environment,159, 426–440.
Cheung, T. C., Schiavon, S., Gall, E. T., Jin, M., and Nazaroff, W. W. (2017). “Longitudinal assessment of thermal and perceived air quality acceptability in relation to temperature, humidity, and CO2 exposure in Singapore”. Building and Environment, 115, 80–90.
Choi, J.-H., and Yeom, D. (2017). “Study of data-driven thermal sensation prediction model as a function of local body skin temperatures in a built environment.” Building and Environment, 121, 130–147.
Cosma, A. C., and Simha, R. (2018). “Thermal comfort modeling in transient conditions using real-time local body temperature extraction with a thermographic camera.” Building and Environment, 143, 36–47.
Dai, C., Zhang, H., Arens, E., and Lian, Z. (2017). “Machine learning approaches to predict thermal demands using skin temperatures: steady-state conditions.” Building and Environment, 1–10.
Ghahramani, A., Tang, C., and Becerik-Gerber, B. (2015). “An online learning approach for quantifying personalized thermal comfort via adaptive stochastic modeling.” Building and Environment, 92,86–96.
He, K., Zhang, X., Ren, S., and Sun, J. (2016). “Deep Residual Learning for Image Recognition.” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778, doi: https://doi.org/10.1109/CVPR.2016.90.
Huang, J., Rathod, V., Sun, C., Zhu, M., Korattikara, A., Fathi, A., and Murphy, K. (2016). “Speed/accuracy tradeoffs for modern convolutional object detectors”.
Jazizadeh, F., and Jung, W. (2018). “Personalized thermal comfort inference using RGB video images for distributed HVAC control.” Applied Energy,220, pp. 829–841.
Jiang, L., and Yao, R. (2016). “Modelling personal thermal sensations using C-Support Vector Classification (C-SVC) algorithm.” Building and Environment,99,98–106.
Meier, A., Dyer, W., and Graham, C. (2017). “Using human gestures to control a building’s heating and cooling system”. Proc. of the 9th International Conference on Energy Efficiency in Domestic Appliances and Lighting (EEDAL), Irvine, California, USA, 13–15.
Pavlin, B., Pernigotto, G., Cappelletti, F., Bison, P., Vidoni, R., and Gasparella, A. (2017). “Real-time monitoring of occupants’ thermal comfort through in-830 infrared imaging: A preliminary study”. Buildings, 7(1).
Ranjan, J., and Scott, J. (2016). “Thermalsense: determining dynamic thermal comfort preferences using thermographic imaging”. Proc. 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, NY, USA, 1212–1222.
Rana, R., Kusy, B., Jurdak, R., Wall, J., and Hu, W. (2013). “Feasibility analysis of using humidex as an indoor thermal comfort predictor”. Energy Bldg., 64, pp. 17–25.
Shaikh, P. H., Mohd. Nor, N. B., Nallagownden, P., Elamvazuthi, I., and Ibrahim, T. (2013). “Robust Stochastic Control Model for Energy and Comfort Management of Buildings”. Australian Journal of Basic and Applied Sciences, 137–144.
EIA (U.S. Energy Information Administration). (2021). “Monthly energy review: August 21”. U.S. Department of Energy. Washington, DC. https://www.eia.gov/totalenergy/data/monthly/pdf/mer.pdf.
Yang, B., Cheng, X., Dengxin, D., Olofsson, T., Li, H., and Meier, A. (2018). “Macro pose based non-invasive thermal comfort perception for energy efficiency.”.
Yang, B., Cheng, X., Dengxin, 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,162.
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Construction Research Congress 2022
Pages: 185 - 194
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Published online: Mar 7, 2022
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