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Building archetypes, representative models of building stock, are crucial forprecise energy simulations in Urban Building Energy Modeling. The currentwidely 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 approachthat leverages representation learning to extract geometric features from aspecific building stock. Built upon VQ-AE, MARL encodes building footprints andpurifies geometric information into latent vectors constrained by multiplearchitectural downstream tasks. These tailored representations are provenvaluable for further clustering and building energy modeling. The advantages ofour algorithm are its adaptability with respect to the different buildingfootprint sizes, the ability for automatic generation across multi-scaleregions, and the preservation of geometric features across neighborhoods andlocal ecologies. In our study spanning five regions in LA County, we show MARLsurpasses both conventional and VQ-AE extracted archetypes in performance.Results demonstrate that geometric feature embeddings significantly improve theaccuracy and reliability of energy consumption estimates. Code, dataset andtrained models are publicly available:https://github.com/ZixunHuang1997/MARL-BuildingEnergyEstimation

MARL: Multi-scale Archetype Representation Learning for Urban Building Energy Modeling