This repository contains Latex assets, graphics and data used for a publication for CISBAT 2019 – International Scientific Conference on SDE4 Learning Trail project by Building and Urban Data Science Lab (https://www.budslab.org/).

Check out the application at: http://www.learningtrail.io/

Find an overview of the content for the six trails at SDE4, Net Zero Energy Building at: http://learningtrail.me/

Please get in touch with [email protected] if you are be interested to find out more details regarding the application or see potential to collaborate with the authors to implement Learning Trail in other locations.

Publication Title: The SDE4 Learning Trail: Crowdsourcing occupant comfort feedback at a netzero energy building

T. Sood, M. Quintana, P. Jayathissa, M. AbdelRahman, C. Miller Building and Urban Data Science (BUDS) Group, Department of Building, School of Design and Environment (SDE), National University of Singapore (NUS), Singapore 117566

This study describes a human-building interaction framework called the \emph{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 \emph{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 \emph{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.