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.

通过自我监督学习并融入建筑外观特征和文化细粒度，本研究提出一种新型工具，以地域特点为基础生成适用于不同建筑清单的代表性建筑原型，可提高能源模拟的精确性和适用性，从而鼓励探索新兴的本地生态系统。