Adaptive thermal comfort suggests that a human connection to the outdoors and control over the immediate environment allow them to adapt to (and even prefer) a wider range of thermal conditions than is generally considered comfortable. Figure 15 shows the rResearch being conducted that enables the quantification of air movement on occupants in mixed-mode ventilated buildings. This research is also in time with increasing interests in hybrid air-conditioning systems, which proposes to achieve energy savings by raising the cooling setpoint. Ceiling fans are then used to offset any discomfort caused by warmer temperature by providing elevated air movement.

Figure 15 Research conducted with Hybrid Cooling Analysis Tool (developed by Mahmoud AbdelRahman and Sicheng Zhan, Ph.D. Students, NUS Department of Building).

With the emergence of the Internet of Things (IoT), it has become cost-effective to implement a digital twin of a building’s operation. A digital twin, as shown in Figure 16, is a digital replica of the actual physical building or system, created through an integration of real-time monitoring and control, advanced energy modelling, and data analytics. The digital twin acts as a bridge between the physical and digital world, providing a platform for advanced analytics to improve building performance and occupant well-being. Examples of its application include:

  • real-time performance monitoring of building systems against design specifications, and
  • real-time optimisation of a building’s operation to achieve improved building performance and indoor environmental quality.

Figure 16 Creating a digital twin of the building with real-time environmental and occupancy monitoring.

One of the biggest challenges in the evaluation of human satisfaction of the built environment is getting people to interact with the building to provide subjective feedback. Surveys, even online versions, are tedious and impractical for daily use. Research conducted at SDE enables the interaction of occupants with their environments in ways that are useful to them, such as finding and booking a hot-desk space or learning about sustainability features in the buildings on campus. Two smart-phone applications are being launched in SDE to facilitate human-building interaction - SpaceMatch and the SDE4 Learning Trail.

  • Learning Trail, shown in Figure 17, is a simple, easy-to-use smartphone application to help users learn, interact and engage with smart building environments such as SDE4.
  • SpaceMatch, shown in Figure 18, is a spatial recommendation platform that guides building occupants in finding and reserving spaces in co-working and activity-based environments while giving them an outlet for human-building interaction and subjective feedback.

Figure 17 SDE4 Learning Trail – A Human-Building interaction environment for occupants to learn about sustainability features on campus while giving subjective feedback about their comfort.

Figure 18 SpaceMatch - An AI-powered space recommendation application that crowdsources comfort feedback.

Personal comfort preferences can be based on factors such as personality, lifestyle, and physiological-based preferences. Building systems could be much more responsive if they could use these additional preferences in an automated way. Urban design could be influenced by an understanding of the clusters of preferences. Figure 19 shows research that is currently underway to collect data from fixed environmental sensors, various subjective feedback crowdsourcing techniques, and wearable devices to develop a typology of thermal comfort personality profiles.

Figure 19 Thermal comfort personalities are developed using wearable devices that collect physiological and subjective feedback.

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