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:
this https URL

建筑原型、能源模拟、城市建筑能源建模、多尺度原型表示学习和几何特征是本研究的关键词。本文提出了一种基于多尺度原型表示学习的方法来提取具体建筑库的几何特征，并通过多个建筑下游任务约束潜在向量，这些定制化的表示在进一步的聚类和建筑能源建模中被证明是有价值的。在洛杉矶县的五个地区的研究中，我们展示了该方法在性能上超过了传统方法和仅使用 VQ-AE 提取的原型，并证明了几何特征嵌入能够显著提高能耗估计的准确性和可靠性。