Data innovation for zero carbon buildings

SUPERVISORS

Hongda Tian (UTS)

Hao Huang (Buildings Alive)

Craig Roussac (Buildings Alive)

DESCRIPTION

According to the IEA, for the international community to meet the goals of the Paris Agreement, by 2030 all new buildings will need to be ‘zero-carbon ready’ and one fifth of all existing buildings (incl. most institutional real estate) will need to be retrofitted to zero-carbon ready levels. Zero-carbon-ready buildings are essential because they will support the massive growth in renewable energy generation that is required. Flexible demand from buildings will underpin the economic case for the infrastructure investment required to decarbonise the global energy supply system. The scholarship will support research in data science and machine learning, with a focus on commercial building demand forecasting and load optimisation in a dynamic operating environment with potentially conflicting environmental, cost and health objectives. Building owners, occupiers, network operators and electricity retailers all stand to benefit greatly if this ‘built in system capacity’ can be better understood and actively addressed. The research will focus on the application of data science and machine learning to fine-grained datasets associated with operational buildings.