Research on the calculation method of dressing thermal resistance based on infrared thermal imaging technology

With the popularity of intelligent technology, it has become an important trend to control the air conditioning terminal equipment intelligently to build the comfortable indoor environment. Therefore, it is necessary to automatically collect environmental and individual parameters (metabolic rate and clothing thermal resistance) and calculate PMV to predict human thermal sensation in real time. In this process,environmental parameters can be automatically measured and uploaded by the device. The metabolic rate is related to human level of activity . However,clothing thermal resistance is affected by many factors such as environment and level of activity.Taking the clothing thermal resistance as a fixed value according to thermal comfort standards will lead to deviations in thermal sensation prediction. In addition, many Chinese dresses cannot be found corresponding thermal resistance value in the thermal standard. In view of this,this paper studies two algorithms to obtain clothing surface temperature and calculate clothing thermal resistance by taking thermal image of the dressed human body with the infrared thermal imager,and compare them with the method of finding the clothing thermal resistance database in thermal comfort standard.And then,we calculate the PMV(predicted mean vote) using clothing thermal resistance obtained by three methods.The results of PMV and the questionnaire(ASV) are compared to analyze the feasibility of these methods. The results show that the mean deviation between the PMV calculated by one algorithm and the ASV is 0.33 scale when sitting in single-layered dress in summer,and that is 0.45 scale when sitting in multi-layered dress in winter. The results of PMV is significantly correlated with ASV. All of these prove that clothing thermal resistance can be calculated by this non-contact method, and it can be benefit to obtain indoor human thermal sensation and adjust and control thermal environment intelligently.