Impact of thermal control by real-time PMV using estimated occupants personal factors of metabolic rate and clothing insulation

To optimize thermal comfort for occupants’ wellbeing and health care, it's essential to adjust heating and cooling systems in real-time based on occupants' thermal preferences. For this, personal factors affect individual thermal comfort, such as metabolic rate and clothing insulation, should be estimated in real-time. The aim of this research is introducing an intelligent model capable of estimating metabolic rate and clothing insulation values from indoor images, suitable for both single and multi-occupant scenarios. Additionally, a control algorithm considering a real-time predicted mean vote (PMV), was developed using the proposed model, and its implications for thermal comfort and energy efficiency were investigated. Utilizing advanced computer vision methodologies, the model achieved a remarkable 95% training accuracy, and its reliability was further validated through experimentation. Evaluations of the PMV-based algorithm underscored its efficacy in enhancing thermal comfort relative to conventional methods in both individual and multi-occupant settings. Conversely, energy use was contingent upon the personal factors. In group settings, the mode values of metabolic rate and clothing insulation were effective for determining a representative PMV. In conclusion, the real-time PMV-based control represents a pioneering approach to augment thermal comfort using actual occupant data, paving the way for a synergistic balance between comfort augmentation and energy saving.