Development of personalized predicted mean vote based on a real-time clothing insulation recognition system

Abstract

Abstract As people’s standards for indoor environmental quality and life are getting higher and higher, many studies try to find ways to improve indoor thermal comfort through experiments and simulations. Predictive mean voting (PMV) is a widely used indicator, but personal factors used to calculate PMV are not easy to monitor. This study aimed to develop a real-time clothing insulation identification (R-CLO) system to estimate personal PMV. This study is mainly divided into three stages. The first stage is to issue questionnaires to obtain the value of the personal clothing level and analyze the correlation between different external temperatures and the clothing insulation (Icl). The second stage is to build a real-time clothing recognition model and analyze its recognition accuracy. The third stage is to use ANN to establish a clothing insulation transition model and use the outdoor temperature and the results of clothing recognition to estimate Icl. This research also established an experimental environment to verify the system’s feasibility. The results show that the system has more than 80% accuracy rate for clothes recognition, and the accuracy rate of short-sleeved tops, shorts, trousers, and skirts is more than 90%. Moreover, most of the Icl estimated by the ANN have less than 0.1 differences from the value reported by occupants. In the future, this system can also combine occupant behavior recognition to strengthen PMV prediction results to achieve more precise, comfortable air-conditioning control.