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

翻译：建筑原型作为建筑群的代表性模型，对于城市建筑能耗建模中的精确能源模拟至关重要。当前广泛采用的建筑原型基于国家尺度开发，可能忽视局部建筑几何特异性带来的影响。我们提出多尺度原型表示学习（MARL），该方法利用表示学习从特定建筑群中提取几何特征。MARL基于VQ-AE框架，通过编码建筑足迹并将几何信息净化为由多个建筑下游任务约束的潜在向量。这些定制化表示被证明对进一步的聚类分析和建筑能耗建模具有重要价值。本算法的优势在于：对不同建筑足迹尺寸的自适应能力、多尺度区域自动生成能力，以及跨邻里社区和地方生态系统的几何特征保持能力。在覆盖洛杉矶县五个区域的研究中，我们证明MARL在性能上超越传统原型和VQ-AE提取的原型。结果表明，几何特征嵌入显著提升了能耗估算的准确性与可靠性。代码、数据集及预训练模型已在公开平台发布：https://github.com/ZixunHuang1997/MARL-BuildingEnergyEstimation