Encoding Urban Ecologies: Automated Building Archetype Generation
  through Self-Supervised Learning for Energy Modeling
Xinwei Zhuang Zixun Huang Wentao Zeng Luisa Caldas
As the global population and urbanization expand, the building sector has
emerged as the predominant energy consumer and carbon emission contributor. The
need for innovative Urban Building Energy Modeling grows, yet existing building
archetypes often fail to capture the unique attributes of local buildings and
the nuanced distinctions between different cities, jeopardizing the precision
of energy modeling. This paper presents an alternative tool employing
self-supervised learning to distill complex geometric data into representative,
locale-specific archetypes. This study attempts to foster a new paradigm of
interaction with built environments, incorporating local parameters to conduct
bespoke energy simulations at the community level. The catered archetypes can
augment the precision and applicability of energy consumption modeling at
different scales across diverse building inventories. This tool provides a
potential solution that encourages the exploration of emerging local ecologies.
By integrating building envelope characteristics and cultural granularity into
the building archetype generation process, we seek a future where architecture
and urban design are intricately interwoven with the energy sector in shaping
our built environments.