NUS, Johnson Controls invest $3.7m for smart buildings research in Singapore

The National University of Singapore (NUS) and Johnson Controls, a multinational conglomerate focusing on smart, healthy, and sustainable buildings, will embark on a collaboration to carry out research on smart buildings.

(From left) Lam Khee Poh, dean at NUS School of Design and Environment, Yoon Soon Fatt, director of industry engagements and partnerships at NUS Office of the Deputy President (research & technology), Kelvin Wong, CEO at Building and Construction Authority, and Alvin Ng, vice president of digital solutions at Johnson Controls / Photo credit: National University of Singapore

Under the partnership, Johnson Controls will commit about S$5 million (US$3.7 million) into the research program, and teams from both organizations will work together to address industry-wide challenges.

The NUS School of Design and Environment (SDE) will kick off with the first research project in April 2021. The research will use machine learning to accelerate the conversion of data from the internet of things (IoT) into the Brick Schema, a standardizing model for data labels in buildings.

NUS claims that this open-source schema describes smart buildings and their subsystems in a format that enables software to “more easily and quickly” connect into a larger number of buildings. With the establishment of a consistent schema across buildings, the industry is able to understand metadata usage across all building types and to improve overall wellness for its users.

“The status quo is that each building speaks its own language when it comes to IoT. With this research, assistant professor Clayton Miller’s team seeks to create a type of ‘translation engine’ to convert these individual languages into the Brick schema,” said Professor Lam Khee Poh, dean of NUS SDE.

He added that the move could help Singapore develop solutions for sustainable cities and is in line with the Singapore Green Plan 2030.

As it is a labor-intensive process to convert existing metadata schemas into the Brick framework, the team intends to set up a machine learning competition to crowdsource solutions to find the right approach to converting each building’s existing labeling methods into the Brick schema.

Currency converted from Singapore dollar to US dollar: US$1 = S$1.35.