UVIC KNOWLEDGE

Research and discovery at the University of Victoria

VOL 20 NO 6 JUNE 2020

EDGEWISE

Machine learning involves putting computer algorithms to work finding patterns in massive quantities of data. It’s the driving force behind tech giants like Google, Amazon and Netflix, but has many more applications beyond shaping our shopping and surfing habits. Evins’ surrogate modelling work uses machine learning to understand building energy performance so that design professionals can quickly and easily assess different design elements.

Energy “optimization” has been a hot topic for university researchers for more than a decade, but has had little traction in industry. Surrogate modelling provides an opportunity for building designers to achieve optimal designs while working in their usual intuitive way. “It’s very hard to sit down with someone and say, ‘List everything you want in a building,’ which is how optimization works. With surrogate modelling, it isn’t necessary to state the problem like that,” notes Evins.

The data generated for Evins’ surrogate modelling tools is revealing information that no one has seen before. The work is bringing unprecedented insight into the intricate relationship between individual design elements and how that interplay affects energy efficiency.

Surrogate modelling is multidisciplinary. Evins’ team features researchers with backgrounds in mathematics, physics, computer science, architecture and engineering.

Evins’ background is in fluid dynamics, which could prove useful as his research team expands its surrogate modelling work to encompass how air flow in and around buildings affects energy efficiency.