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

Learning transferable surrogates for building performance requires data that expose how geometry and spatial topology interact with environmental forcing, yet existing datasets rarely align these modalities at scale. We present ArchEGraph, a large-scale graph dataset aligning building geometry, topology, weather, and zone-level physical responses across 5,481 buildings and 49,326 validated building–weather simulations. The target is the EnergyPlus-derived external thermal load under fixed constructions and internal schedules, isolating variation attributable to geometry, topology, and weather rather than operational energy use. ArchEGraph supports two structured learning problems: (i) Mesh-to-Graph reconstruction, which infers local adjacency and globally consistent latent space partitions from unordered geometric primitives; and (ii) Graph-to-Energy prediction, which maps a static heterogeneous graph and an 8,760-step weather sequence to node-aligned response trajectories. Standardized protocols evaluate in-distribution performance and generalization across building and climate shifts. Results expose substantial open challenges: current set encoders recover local face adjacency reliably but struggle with global face–space incidence, particularly on irregular manually designed buildings. Moreover, shuffling only face–space assignments within the same architecture increases prediction error, directly demonstrating that correct topology provides useful information beyond model capacity. ArchEGraph thus provides a controlled benchmark for studying structured physical learning and transferable geometric inductive biases.