ArchEGraph：用于几何-拓扑-物理对齐的建筑能源建模的大规模图数据集
ArchEGraph: A Large-Scale Graph Dataset for Geometry-Topology-Physics Aligned Building Energy Modeling
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中文总结 AI 辅助
研究提出ArchEGraph大规模图数据集，定义两个基准任务并开展泛化实验，为建筑能源建模的几何-拓扑-物理耦合研究提供统一测试平台，支持替代模型开发评估。
中文摘要 AI 辅助
准确估算建筑能耗对于实现碳中和与可持续建筑至关重要。为更好地理解设计决策对建筑能耗的影响，并校准可为建筑师和工程师提供快速设计反馈的机器学习模型，需要明确将建筑几何与性能关联的大规模数据集。我们提出ArchEGraph，这是一个大规模基准数据集，将建筑表示为异构图，包含对齐的几何、拓扑、天气及区域级热负荷信息。该数据集包含5481栋建筑和49326个经验证的建筑-天气模拟案例，总计包含超过133000个空间节点和144万个面节点，体现了显著的几何与拓扑复杂性。基于ArchEGraph，我们定义了两个基准任务：（i）从多边形网格进行图重构，旨在从几何表示中恢复拓扑结构；（ii）拓扑感知负荷预测，利用图结构和时间天气条件预测区域级响应时间序列。我们还为这两个任务引入了标准化评估协议，并开展跨建筑、跨气候的泛化实验以评估模型鲁棒性。ArchEGraph为研究建筑能源建模中的几何-拓扑-物理耦合提供了统一测试平台，支持可扩展且可泛化的替代模型的开发与评估。
英文摘要
Accurate estimation of building energy use is essential for achieving carbon neutral and sustainable buildings. To better understand the influence of design decisions on building energy use and calibrate machine learning models that can give architects and engineers rapid design feedback, large-scale datasets are needed that explicitly map building geometry to performance. We present ArchEGraph, a large-scale benchmark dataset that represents buildings as heterogeneous graphs with aligned geometry, topology, weather, and zone-level thermal loads. The dataset contains 5,481 buildings and 49,326 validated building-weather simulation cases. In total, it includes over 133,000 space nodes and 1.44 million face nodes, reflecting substantial geometric and topological complexity. Based on ArchEGraph, we define two benchmark tasks: (i) graph reconstruction from polygonal meshes, aiming to recover topological structure from geometric representations; and (ii) topology-informed load prediction, which leverages graph structure and temporal weather conditions to forecast zone-level response time series. We further introduce standardized evaluation protocols for both tasks and conduct cross-building and cross-climate generalization experiments to assess model robustness. ArchEGraph provides a unified testbed for studying geometry-topology-physics coupling in building energy modeling, enabling the development and evaluation of scalable and generalizable surrogate models.
发表机构
- Tsinghua University（清华大学）
- UC Berkeley（加州大学伯克利分校）
- MIT（麻省理工学院）
机构由 AI 辅助整理，请以论文原文为准。
