<div>
<p>People spend most of their time indoors. Because people’s
health and productivity are highly dependent on the quality of the indoor
thermal environment, it is important to provide occupants with healthy,
comfortable and productive indoor thermal environment. However, inappropriate
thermostat temperature setpoint settings not only wasted large amount of energy
but also make occupants less comfortable. This study intended to develop a new
control strategy for HVAC systems to adjust the thermostat setpoint
automatically and accordingly to provide a more comfortable and satisfactory
thermal environment.</p>
<p>This study first trained an image classification model
based on CNN to classify occupants’ amount of clothing insulation (clothing
level). Because clothing level was related to human thermal comfort, having
this information was helpful when determining the temperature setpoint. By
using this method, this study performed experimental study to collect
comfortable air temperature for different clothing levels. This study collected
450 data points from college student. By using the data points, this study
developed an empirical curve which could be used to calculate comfortable air
temperature for specific clothing level. The results obtained by using this curve
could provide environments that had small average dissatisfaction and average
thermal sensation closed to neutral.</p>
<p>To adjust the setpoint temperature according to
occupants’ thermal comfort, this study used mean facial skin temperature as an
indicator to determine the thermal comfort. Because when human feel hot, their
body temperature would rise and vice versa. To determine the correlation, we
used a long wave infrared (LWIR) camera to non-invasively obtain occupant’s
facial thermal map. By processing the thermal map with Haar-cascade face
detection program, occupant’s mean facial skin temperature was calculated. By
using this method, this study performed experimental study to collect
occupant’s mean facial skin temperature under different thermal environment.
This study collected 225 data points
from college students. By using the data points, this study discovered
different intervals of mean facial skin temperature under different thermal
environment. </p>
<p>Lastly, this study used the data collected from
previous two investigations and developed a control platform as well as the
control logic for a single occupant office to achieve the objective. The
measured clothing level using image classification was used to determine the
temperature setpoint. According to the measured mean facial skin temperature,
the setpoint could be further adjusted automatically to make occupant more
comfortable. This study performed 22 test sessions to validate the new control
strategy. The results showed 91% of the tested subjects felt neutral in the
office</p>
</div>
<br>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/12152343 |
| Date | 20 April 2020 |
| Creators | Xuan Li (8731800) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/ADVANCED_INDOOR_THERMAL_ENVIRONMENT_CONTROL_USING_OCCUPANT_S_MEAN_FACIAL_SKIN_TEMPERATURE_AND_CLOTHING_LEVEL/12152343 |
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