Optimization of computational time for Urban Building Energy Modelling, through generalizing the Building Footprint. A case study in London, UK.

The reconfiguration of everyday human activities is considered necessary to tackle the challenge of climate change. Building stock is one of the major contributors to urban energy consumption and targeting its energy performance through upgrade could help policy makers to reach the energy reduction goals. Given the rapidity of climate change, Urban Building Energy Modelling (UBEM) is
needed. However, gathering the essential data for UBEM is a challenge, and the simplification of its process has become one-way solution. This study focuses on the Level of Generalization (LoG) of the building footprint (individual, local, district), that is needed. Preliminary results show the higher the LoG, the higher the underestimation of energy demand.