PMV-based optimization of bedding thermal resistance combinations for year-round sleep demand

In regions characterized by hot summers and cold winters, significant annual climate fluctuations often lead residents to rely on empirical practices, such as stocking multiple quilts with varying thermal resistance to maintain thermal comfort during sleep, in the absence of quantitative guidance. This study develops a coupled thermal comfort model for bedding systems in sleep environments by modifying the Predicted Mean Vote (PMV) model based on human heat balance equations. Using hourly temperature data (8760 h) from a typical meteorological year in a representative city, sinusoidal temperature distribution curves were obtained via data fitting. These curves were then used to numerically invert the hourly thermal resistance requirements of bedding systems needed to maintain a PMV within the comfort range of −0.5 to + 0.5, under varying ambient temperature and humidity conditions. Results reveal that ambient temperature has a significantly greater impact on bedding thermal resistance needs than relative humidity. The thermal resistance demand follows a distinct U-shaped annual pattern: lowest during summer (July–August), highest in winter (December–March), and moderate during transitional seasons. Optimization using a genetic algorithm suggests that a combination of three quilts with thermal resistances of 1 clo, 2 clo, and 5.5 clo can achieve 100% annual thermal comfort coverage. This configuration involves single-layer use during summer, double layers in spring and autumn (April–June and September–November), and triple layers in winter. These findings offer a scientific basis for household bedding selection and home textile product design in hot-summer/cold-winter climate zones. Unlike previous PMV-related sleep thermal comfort studies, this work integrates a sleep heat balance model with a PMV-based inversion framework and full-year hourly meteorological data to derive bedding thermal resistance requirements.