Labelled human comfort data can be a valuable resource in optimising the built environment, and improving the wellbeing of individual occupants. The acquisition of labelled data however remains a challenge. This paper presents a methodology for the collection of in-situ occupant feedback data using a Fitbit smartwatch. The clock-face application cozie can be downloaded free-of-charge on the Fitbit store and tailored to fit a range of occupant comfort related experiments. In the initial trial of the app, fifteen users were given a smartwatch for one month and were prompted to give feedback on their thermal preferences. In one month, with minimal administrative overhead, 1460 labelled responses were collected. This paper demonstrates how these large data sets of human feedback can be analysed to reveal a range of results from building anomalies, occupant behaviour, occupant personality clustering, and general feedback related to the building. The paper also discusses limitations in the approach and the next phase of design of the platform.
The activity-based workspace (ABW) paradigm is becoming more popular in commercial office spaces. In this strategy, occupants are given a choice of spaces to do their work and personal activities on a day-to-day basis. This paper shows the implementation and testing of the Spacematch platform that was designed to improve the allocation and management of ABW. An experiment was implemented to test the ability to characterize the preferences of occupants to match them with suitable environmentally-comfortable and spatially-efficient flexible workspaces. This approach connects occupants with a catalog of available work desks using a web-based mobile application and enables them to provide real-time environmental feedback. In this work, we tested the ability for this feedback data to be merged with indoor environmental values from Internet-of-Things (IoT) sensors to optimize space and energy use by grouping occupants with similar preferences. This paper outlines a case study implementation of this platform on two office buildings. This deployment collected 1,182 responses from 25 field-based research participants over a 30-day study. From this initial data set, the results show that the ABW occupants can be segmented into specific types of users based on their accumulated preference data, and matching preferences can be derived to build a recommendation platform.
This study describes a human-building interaction framework called the SDE Learning Trail, a mobile app that is currently deployed at the SDE4 building - the new Net Zero Energy Building (NZEB) at the National University of Singapore (NUS). This framework enables building occupants and visitors to learn about the well and green features of the new NZEB while facilitating collection of environmental comfort feedback in a simple and intuitive way. Within just three months, 1163 feedback responses of thermal, visual and aural comfort were obtained. A total of 616 participants have contributed to the study till date, with 79 participants who provided five or more instances of feedback. This data set provides new opportunities for understanding occupant comfort behavior through supervised and unsupervised data-driven methods. This paper demonstrates how occupants can be clustered into comfort personality types that could be used as a foundation for prediction and recommendation systems that use real-time occupant behavior instead of rigid comfort models. We provide an overview of the application methodology and initial results in the SDE4 building.
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