Funded by Grosvenor Engineering Group, Enviro Building Services and the NSW Department of Industry, the project is part of SMART’s Digital Living Lab, which provides an Internet of Things (IoT) Network to create smarter living in buildings.

Senior research fellow Dr Rohan Wickramasuriya is leading the team which is looking at ways of optimising the heating, ventilation and cooling of buildings under the Building Energy Monitoring project.

Anonymised real building data is collected from equipment maintained by Grosvenor, while Enviro’s office spaces are providing image data, which will be used to train deep neural networks to predict outcomes.

"The project’s focus is to increase the efficiency of building environments," Dr Wickramasuriya said.

"For instance, by forecasting room temperatures as a function of external and internal conditions, we expect to find that it is more efficient to pump cool night air into a building, rather than turning off the system at 6pm and allowing rooms to heat up due to lack of ventilation."

The research will investigate image recognition-based room occupation detection; application of deep learning neural networks for room temperature forecasting; vibration sensor fault diagnosis prototype and visualising results using an online dashboard.

It aims to solve three key practical problems in building management - accurate counting of building/room occupation; accurate forecasting of indoor temperature to help assess the impact of different power regimes and mode of operations for the HVAC system and build a smart sensor that can detect issues in rotating equipment before they become problems.

"Outcomes expected from this project​ include a readily deployable, accurate and IoT compliant people counter; accurate indoor temperature forecasting algorithm and a prototype vibration sensor," Dr Wickramasuriya said.

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Grosvenor national sustainability manager Rod Kington said the company was focused on making buildings smarter and more productive.

"We were drawn to the UOW’s Smart Infrastructure facility as our business goals are aligned," he said.

"We are expecting to further enhance and innovate within the built environment from the insights gained from this research.

"Our main purpose is to make buildings operate more efficiently by driving down inputs from labour, energy, water and carbon dioxide.

"The research into deep neural networks moves us down the pathway toward machine learning and artificial intelligence which will be significant future drivers of value when maintaining building assets."

Project results are expected to be delivered mid-2019.