DEVELOPMENT OF A THERMAL COMFORT INDEX FOR SEDENTARY WORK ENVIRONMENT

Abstract

Subtropical regions are witnessing unprecedented variability in weather conditions coupled with heat waves. Furthermore, providing human thermal comfort indoors in such conditions became cumbersome in the dichotomous relationship between climate change and building energy efficiency. Existing international standards suggest strict thermal comfort compliance following the realities of the Global North. In this context, this research addresses a research question: How can a thermal comfort index developed in a controlled condition for the subtropical regions be extended to natural conditions? To address it, a thermal comfort experiment is conducted in a controlled climate chamber. In addition, a similar study is conducted in a real-world setting, during Summer, Winter and Monsoon seasons. Results of thermal comfort experiments established the sensitivity of subjects from subtropical regions, neutral temperature, comfort temperature and preferred temperature, for different air temperature, relative humidity and air velocity set points in climate chamber as well as the real-world conditions. A multiple thermal environmental variablebased empirical index is formulated, which can be extended to real-world settings. Furthermore, the effect of different seasons and gender is analyzed. A rational adaptive index Caliditychamber is developed, which can be used for determining thermoneutrality for specific contexts. A comparative assessment of chamber experiments and field surveys elucidated the effect of air velocity in alleviating thermal discomfort. Further, to evaluate the impact of air velocity on human thermal comfort, experiments are conducted in a controlled climate chamber. A reduction in thermal sensation is observed with the provision of air velocity. Thermal preferences among gender, Air velocity class and modulation group exhibited significant differences. Based on the user preferences for air velocity in different Ta and RH set points, a predictive model is developed using Machine Learning algorithm. An IoT-based air velocity control device is designed and prototyped. The machine learning based air velocity predictive model is implemented in this device and its application is demonstrated. This research led to the formulating of a thermal comfort index for a sedentary work environment, which can be implemented in air-conditioned or mixed-mode buildings. An adaptive-rational index can be helpful in determining context-specific neutral temperature requirements. Furthermore, an ML-based air velocity prediction model can alleviate thermal discomfort by reducing a warm thermal sensation.