Development and performance evaluation of an indoor thermal environment control algorithm incorporating MET estimation model with object detection

As people increasingly spend time indoors, the significance of the indoor environment in influencing occupant quality of life is becoming more pronounced. Traditionally, indoor environment control primarily relied on fixed temperature settings, which failed to accommodate the diverse circumstances of occupants. This approach limited the creation of a comfortable indoor environment and the enhancement of energy efficiency. Consequently, there is growing interest in occupant-centric control (OCC), which integrates metabolic rate (MET) information, which is a critical factor in determining the thermal sensation of occupants. Previously, a method was developed to estimate MET by classifying occupant poses and detecting the objects they interact with from indoor images. This study aims to develop and experimentally validate an indoor thermal environment control algorithm (ITEC-algorithm) using the MET estimation model and assess its effectiveness and applicability in real building environments.

The performance evaluation revealed that the ITEC-algorithm significantly enhanced the comfort ratios, achieving improvements of up to 59% compared to the fixed temperature control and 28% compared to the control methods that only used the pose classification model for MET estimation. The energy consumption varied depending on the activity and control method, with a reduction of up to 88% compared to fixed temperature control. These results indicate that thermal comfort can be enhanced while minimizing unnecessary energy consumption by incorporating the MET of the occupants. Consequently, it has been confirmed that the ITEC-algorithm effectively improves thermal comfort by managing the MET of various occupants.