Sensor Locations for Occupant Thermal Comfort State Prediction

Indoor air temperature, which is one of the main factors affecting the thermal comfort of occupants, varies across locations/spaces. However, current occupant thermal comfort models rely on predefined formulas or data-driven approaches that often ignore the importance of the specific location in the room at which the sensor is placed. This research aims to study the impact of sensor location on occupant thermal comfort state prediction. A set of 90-min occupant experiments were conducted in a controlled environment. Multiple temperature and humidity sensors were placed at different locations in the room. During the experiments, the room temperature changed from 19°C to 29°C, and the humidity, mean radiant temperature, and wind speed were controlled. The subjects performed office duties and provided feedback about their thermal comfort periodically. Personal parameter data were also collected. For each sensor location, a thermal comfort state model was developed using the XGBoost algorithm. Each model was tested in predicting the occupant comfort state using temperature and humidity data from other room locations. The results showed that the location of indoor parameter data used for prediction could affect model performances by up to ±7.2% accuracy and ±8.0% F1-measure.