Gaby Baasch (University of Victoria)
The existing building stock accounts for over 30% of global carbon emissions and energy demand. Effective building retrofits are therefore vital in reducing global emissions. Current methods for building energy assessment typically rely on walk-throughs, surveys or the collection of in-situ measurements, none of which are scalable or cost effective. Supervised machine learning methods have the potential to overcome these issues, but their application to retrofit analysis has been limited. This paper serves as a novel showcase for how multivariate time series analysis with Gated Recurrent Units can be applied to targeted retrofit analysis via two case studies: (1) classification of building heating system type and (2) prediction of building envelope thermal properties.