Encoding Urban Ecologies: Automated Building Archetype Generation through Self-Supervised Learning for Energy Modeling

arXiv:2404.07435v1 Announce Type: new
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 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. …

abstract arxiv automated building carbon consumer contributor cs.cv encoding energy expand global modeling population sector self-supervised learning supervised learning through type urban