MARL: Multi-scale Archetype Representation Learning for Urban Building
  Energy Modeling
Xinwei Zhuang Zixun Huang Wentao Zeng Luisa Caldas
Building archetypes, representative models of building stock, are crucial for
precise energy simulations in Urban Building Energy Modeling. The current
widely adopted building archetypes are developed on a nationwide scale,
potentially neglecting the impact of local buildings' geometric specificities.
We present Multi-scale Archetype Representation Learning (MARL), an approach
that leverages representation learning to extract geometric features from a
specific building stock. Built upon VQ-AE, MARL encodes building footprints and
purifies geometric information into latent vectors constrained by multiple
architectural downstream tasks. These tailored representations are proven
valuable for further clustering and building energy modeling. The advantages of
our algorithm are its adaptability with respect to the different building
footprint sizes, the ability for automatic generation across multi-scale
regions, and the preservation of geometric features across neighborhoods and
local ecologies. In our study spanning five regions in LA County, we show MARL
surpasses both conventional and VQ-AE extracted archetypes in performance.
Results demonstrate that geometric feature embeddings significantly improve the
accuracy and reliability of energy consumption estimates. Code, dataset and
trained models are publicly available:
https://github.com/ZixunHuang1997/MARL-BuildingEnergyEstimation