Indoor clothing insulation and thermal history: A clothing model based on logistic function and running mean outdoor temperature

Abstract Predicting the pattern of clothing adjustment to climate change can provide important basis for thermal comfort and energy consumption analysis. This study proposed a clothing model (IC-RM model) to predict indoor clothing insulation based on people's thermal history. In the IC-RM model, the running mean (RM) outdoor temperature (exponentially weighted running mean of the past outdoor temperatures) was used as the outdoor climate index to reflect the thermal history. Different from the existing models, the IC-RM model adopted a four parameters logistic function to fit the relation between indoor clothing insulation and the RM outdoor temperature. A longitudinal thermal comfort survey (13 months) was conducted in two different types of naturally ventilated building in Changsha China. The decreased freedom of clothing adjustment at high/low outdoor temperatures and notable effects of the past outdoor temperatures on the indoor clothing insulation were observed. The IC-RM model was implemented using 1427 useful clothing records collected during the survey. The high R2 value (>0.9) for the IC-RM model indicated that the proposed model provides an effective method to quantify the change of indoor clothing insulation based on the effect of thermal history. Compared with linear, exponential and power functions, the logistic function exhibited better performance in quantifying the tendency for the variation in the indoor clothing insulation with the RM outdoor temperature.

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