Encoding Urban Ecologies

01 Jun, 2024

Encoding Urban Ecologies

BCNM student Xinwei Zhuang and BCNM faculty Luisa Caldas with Zixun Huang and Wentao Zeng presented "Encoding Urban Ecologies: Automated Building Archetype Generation through Self-Supervised Learning for Energy Modeling" in Acadia 2023.

From the abstract:

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 dierent 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-specic 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 the dierent 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.

Read the article here!