Predictive clothing insulation model based on outdoor air and indoor operative temperatures
Abstract
Clothing affects people’s perception of the thermal environment. In this research two predictive models of clothing insulation have been developed based on 6,333 selected observations taken from ASHRAE RP-884 and RP-921 databases. The database has been used to statistically analyze the influence of 20 variables on clothing insulation.
The results show that the median clothing insulation is 0.59 clo (0.50 clo (n=2,760) in summer and 0.66 clo (n=3,580) in winter). Clothing insulation is correlated with outdoor air temperature (r=0.45), operative temperature (r=0.3), relative humidity (r=0.26), air velocity (r=0.14) and metabolic activity (r=0.12).
Two mixed regression models were developed. In the first one clothing insulation is a function of outdoor air temperature measured at 6 o’clock in the morning and in the second one the influence of indoor operative temperature is also taken into account. The models were able to predict only 19 and 22% of the total variance, respectively. These low predicting powers are better than the assumption of constant clothing insulation for the heating (1 clo) and cooling (0.5 clo) seasons.