UNDERSTANDING OF INDIVIDUALISED HUMAN COMFORT IN A NET-ZERO ENERGY, WELL CERTIFIED BUILDING

Abstract

With a shift of focus from normal buildings to Net-zero energy buildings, it led to the necessity to research occupants’ comfort in those buildings. Occupants being the end- users of the building during operation and maintenance phase, their comfort is of the utmost importance. Thermal comfort will, therefore, be the key focus since it is a highly subjective topic and affects individual comfort the most.

As SDE4 being a Net-zero energy, WELL building, experiment was conducted in pre-set rooms to find out occupant’s thermal comfort. The same process was conducted in a normal building, SDE2 to understand participants’ comfort level too. Wearable technology, Fitbit was the main source to collect the relevant data such as participants’ heart rate, location, time and thermal comfort feedback since it is convenient and have the ability to measure data accurately.

Additionally, a programming language, Python was used to clean and analyse the data collected. Individual thermal satisfaction was analysed to understand thermal comfort of users on a personal level as recommended set-points in the given environment might not be as recommended. Graphical representation was generated where it shows individual thermal comfort based on heart rate and time of the day. Comparison between participants’ thermal comfort in SDE2 and SDE4 was carried out according to the location data to have an in-depth understanding of the behaviour.

It was found that in general, 86.1% of the individuals are thermally satisfied in a Net-zero energy, WELL building. Whereas, improvements can be made to a normal building where the temperature range is lower. This study concludes that wearable technology can collect useful data and individual comfort should not be compromised and it is possible for the room to cater to individual thermal comfort.

Keywords: Individualised thermal comfort, Net-zero energy buildings, WELL buildings, Wearable technology, Heart rate, Fitbit, Python