An automated and explainable machine learning framework for multi-objective energy performance evaluation and early-stage design support of indoor arenas

Indoor arenas, as large-span public buildings, entail high construction and operational costs, particularly in energy use. The pursuit of iconic forms often overshadows rational, performance-driven design, while their complex geometry and diverse functions make energy performance evaluation challenging at early design stages. This study develops an integrated framework that combines parametric simulation, a stacked-ensemble machine learning model, and SHAP (SHapley Additive exPlanations)-based interpretation to relate geometric configurations to the energy performance of indoor arenas. An automated performance evaluation pipeline integrates multiple evaluation modules, enabling automated data transfer, processing, and large-scale analysis. Using partially stratified and Latin Hypercube sampling, 9667 design scenarios were generated across three representative climate zones. Five indicators in three climate zones were evaluated, including energy use intensity (EUI) for cooling, heating, lighting, and total energy, and energy yield intensity (EYI). The ensemble models achieved high predictive accuracy (average R2 > 0.95), providing a reliable basis for interpretation. SHAP analysis reveals the varying contributions of design parameters to each performance indicator across climates, as well as the nonlinear interaction mechanisms between complex roof morphology and overall energy behavior. These findings establish climate-based strategies for balancing energy consumption and generation, and demonstrate an integrated and interpretable control workflow for performance-informed design and analytical support for performance-oriented design decisions in complex-built systems.