ADVANCED INDOOR THERMAL ENVIRONMENT CONTROL USING OCCUPANT’S MEAN FACIAL SKIN TEMPERATURE AND CLOTHING LEVEL

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.

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.

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.

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