A scalable probabilistic meta-model framework for accelerating Monte Carlo-based thermal performance analysis of small-to-medium-sized commercial buildings in six European regions under climate uncertainty

Quantifying building thermal performance under uncertainty remains a major computational challenge, particularly when traditional Monte Carlo (MC) simulations are applied to large-scale probabilistic analyses. To address this limitation, this study develops a scalable and generalizable data-driven stochastic framework that accelerates MC-based convergence analysis for building energy modeling with a focus on low-to-medium-sized commercial buildings in six cities across Europe to test the method. The proposed approach combines Latin Hypercube Sampling with an optimized tree-based meta-model ensemble tuned via Bayesian Optimization, replicating MC convergence behavior with up to 95–99% reduction in computation time while maintaining high accuracy (average R 2 > 0.90). By using 700 MC baseline samples, more than 600,000 stochastic samples are generated to demonstrate the framework’s ability to support high-resolution uncertainty and sensitivity analyses at scales impractical for direct MC simulations. Using a 0.1% Coefficient of Variation threshold, the study reveals that convergence characteristics of key output percentiles (5 th , 50 th , and 95 th ) vary systematically with building typology, climate, and scenario. Sensitivity analysis identifies the solar heat-gain coefficient of south-facing glazing as the dominant factor influencing future cooling demand, while persistent heating demand in cold climates highlights the need for balanced adaptation strategies. Overall, the proposed framework constitutes a robust and transferable methodology for efficient stochastic assessment of building thermal performance under climate uncertainty, offering a foundation for robust energy policy and design decisions. • A probabilistic method combined with meta-modeling is proposed. • Meta-modeling methods give similar results to the MC method, but more effective than MC. • The meta-model accelerates the convergence rate for large-scale simulations. • Building envelopes have the main influence on changes in future energy demand.