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Thermal comfort is a significant factor in the indoor building environment because it influences both human productivity and health. A currently popular method for predicting thermal comfort levels, the Predicted Mean Vote (PMV) and Predicted Percent Dissatisfied (PPD) model, unfortunately, has certain limitations. Consequently, the development of a better method for making accurate predictions (especially for individuals) is needed. Our goal was to develop a tool to predict individual thermal comfort preferences and automatically control the heating, ventilation, and air conditioning (HVAC) systems. This study adopted a series of human-subject experiments to collect essential data. All collected data was analyzed by adopting different machine learning algorithms. The machine learning algorithms predicted individual thermal comfort levels and thermal sensations, based on facial skin temperatures of participants in the experiments. These predictions were input data for the HVAC system control model, and results supported the potential for using facial skin temperatures to predict thermal comfort and thermal sensation levels. Moreover, this tool provided automatic control of the HVAC systems that can help improve the indoor environment of a building.
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Low-Energy Wearable Cooling Strategy for Thermal Comfort at a Warm Environment
The objective of this study is to assess the effectiveness of wearable cooling in improving thermal comfort for a warm environment that would become prevalent due to more frequent extreme weather events, especially when air conditioning is not accessible for many developing countries. The experiment was conducted in an environment room with air temperature maintained at 31 °C and relative humidity at 55%. The study tested 30 participants using a wearable cooling device at the upper back location, while another 30 had no local cooling as the control group. Participants’ thermal comfort, thermal sensation and other metrics were assessed three times for a test session. The clothing insulation was 0.36 clo to simulate summer attire. The results showed significantly lower average local and whole-body thermal sensation for the participants with the wearable cooling device than the control group by considering all the votes during the entire session. Compared to the baseline, in particular, the local cooling group indicated a significant reduction in local thermal sensation for all three times of self-evaluation. Nevertheless, the reduction in overall thermal sensation occurred right after the local cooling was applied. Such a significant reduction was not observed after a while and then emerged again during the test, indicating an interactive phenomenon involving thermal adaptation and comfort restoration which will be investigated in the future.
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- Award ID(s):
- 1931077
- PAR ID:
- 10374220
- Date Published:
- Journal Name:
- The 5th International Conference on Building Energy & Environment (COBEE)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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