ArchEGraph: A Large-Scale Graph Dataset for Geometry-Topology-Physics Aligned Building Energy Modeling

2026-08-06

AI Summary

This work addresses the limitation in current building energy modeling caused by the absence of large-scale datasets that explicitly link geometric, topological, and physical properties. The authors introduce ArchEGraph, the first large-scale graph-based building energy dataset that aligns geometry, topology, and physics by representing buildings as heterogeneous graphs incorporating spaces, surfaces, weather conditions, and thermal loads. The dataset enables two benchmark tasks: graph reconstruction and topology-aware load prediction. ArchEGraph encompasses 5,481 buildings, 49,326 simulation cases, 133,000 space nodes, and 1.44 million surface nodes, supporting generalization evaluations across buildings and climates. It facilitates research on scalable surrogate models, and its standardized protocols validate both the effectiveness of the proposed tasks and the robustness of evaluated models.

Abstract

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.

Problem

• building energy modeling
• geometry-topology-physics alignment
• large-scale dataset
• heterogeneous graph
• thermal load prediction

Innovation

• heterogeneous graph
• geometry-topology-physics alignment
• building energy modeling
• graph reconstruction
• load prediction