On demand prediction model of the near-body microenvironment for personalized heating,ventilation,and air conditioning control

Abstract

Accurate forecasting of individual thermal demand is essential for achieving personalized and energy-efficient control in heating, ventilation, and air conditioning (HVAC) systems. Traditional control strategies and thermal comfort models often rely on generalized assumptions or subjective surveys, which fail to capture individual variability. In this paper, we propose a demand forecasting model that uses physiological and microenvironmental parameters to infer personalized thermal needs in real-time. This study used skin temperature as the core physiological indicator, continuously collects physiological and microenvironment parameters of the wrist, and systematically analysed the relationship between skin temperature and environmental parameters. The results indicated that environmental temperature is the main factor affecting skin temperature, and there was a strong linear correlation between the two. Furthermore, the regression relationship between wrist skin temperature and environmental temperature differed during the temperature-falling and temperature-rising phases, reflecting different physiological responses. The research findings suggest that the coupled relationship between skin temperature and environmental parameters can provide important data support for individual thermal demand prediction in HVAC control.