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Life sciences · Preprint
arXiv · August 7, 2026
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ArchEGraph is a large-scale computational dataset of 5,481 buildings represented as heterogeneous graphs, designed to support machine learning surrogate models for building energy prediction. The study defines two benchmark tasks (graph reconstruction and topology-informed load prediction) and includes cross-building and cross-climate generalization experiments, but does not report validation accuracy, comparison to existing methods, or real-world performance correlation.
Dataset development and benchmark study. 5,481 simulated buildings with zone-level thermal load data across multiple weather conditions. Intervention: ArchEGraph dataset with heterogeneous graph representation and benchmark task definitions. n = 5,481.
Dataset comprises 5,481 buildings and 49,326 validated building-weather simulation cases Dataset includes over 133,000 space nodes and 1.44 million face nodes Two benchmark tasks defined: graph reconstruction from polygonal meshes and topology-informed load prediction
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The source did not state who this applies to in practice.
This is a dataset paper and benchmarking study with no clinical or patient outcomes; it describes computational infrastructure for building energy modeling without reporting validation against real-world building performance or clinical utility.
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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.
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