Application of Artificial Intelligence to Thermal Comfort Sensor App for New-Generation Smart HVAC Systems
Project Description

Most HVAC systems only use temperature sensors to control the actuators, Fan Coil Units in every classrooms and offices at HKUST. On the other hand, human thermal comfort sensors which typically many parameters, such as temperature, humidity, air speed, heat radiation, human activity and the clothes condition. Predict Mean Vote (PMV) is one of the common thermal comfort indices used to predict statistical thermal comfort response based on the seven-points thermal sensation scale according to the industry standard, ASHARE Standard 55. Thermal comfort sensing using PMV was established by American and European experiments involving over around thousand human subjects. According to our preliminary study at HKUST, the results of the PMV sensor module using ASHARE Standard needs to be improved by conducting more human subject experiments.

LEE Yi-Kuen
Course type
Applicant's Roles

This project is to apply the open-source deep learning algorithm and the CMOS image sensor of smartphone to monitoring the user’s activity/metabolic rate which is one important parameter to enhance the accuracy of thermal comfort sensing. The App will be connected to an existing IoT server at HKUST for statistical analysis of human thermal comfort to improve the PMV model for Hong Kong’s environment.

Applicant's Learning Objectives

Develop basic understanding of commercial HVAC systems, human thermal comfort, PMV sensors

Hands-on experience to develop a smartphone App

Collaboration with HKUST-MIT Smart EeB project team working on the same project.

Complexity of the project