To meet carbon reduction goals, municipalities, universities, and other large organizations need reliable and adaptable models that provide detailed building performance metrics to efficiently manage future energy demand. At the urban scale, models are often restricted by access to usage and geometry data and can only accurately predict aggregate energy demand based on historic data or statistical models.

This paper seeks to present a novel workflow that uses institutional GIS datasets to produce calibrated multi-zone energy models for future scenario assessment and to inform building retrofitting options. The authors hope that the introduced workflow leads to a wider adoption of the involved tools to support the environmental strategies of others.