Developing Thermal Comfort Level Analysis System using Predicted Mean Vote (PMV) and Building Information Modeling (BIM)

Recently there has been growing recognition of the value of Building Information
Modeling (BIM) in the Architecture, Engineering and Construction (AEC) industry.
However, building information are often displayed in text or 2D graph only, making it
hard for the management to clearly understand the thermal environment of the building.
While BIM model may present the thermal comfort in a three-dimensional space, it is
usually limited by the skills requirement of the workers and its incapability to support
real-time data that is constantly changing. In order to make BIM technology more
valuable in the Operational and Management (O&M) stage of building, this project
had developed a thermal comfort level analysis system by applying Internet-of-Things
(IoT) technology to collect data from sensors, then analyse them to determine thermal
comfort level, and present it in the BIM model through the webpage platform provided
by Autodesk Forge. To determine the thermal comfort level, the Predicted Mean Vote
(PMV) model is used to compute the thermal comfort level based on 6 primary
influencing factors which consists of Dry Bulb Temperature, Mean Radiant
Temperature, Relative Humidity, Air Velocity, Metabolic Rate and Clothing Level.