This work presents a scalable Bayesian modeling framework for evaluating building energy performance using smart-meter data from 2,788 Danish single-family homes. The framework leverages Bayesian statistical inference integrated with Energy Signature (ES) models to characterize thermal performance in buildings. This approach quantifies key parameters such as the Heat Loss Coefficient (HLC), solar gain, and wind infiltration, while providing full posterior distributions to reflect parameter uncertainty. Three model variants are developed: a baseline ES model, an auto-regressive model (ARX-ES) to account for thermal inertia, and an auto-regressive moving average model (ARMAX-ES) that approximates stochastic gray-box dynamics. Results show that model complexity improves one-step-ahead predictive performance, with the ARMAX-ES model achieving a median Bayesian R^2 of 0.94 across the building stock. At the single-building level, the Bayesian approach yields credible intervals for yearly energy demand within $\pm1\%$, enabling more robust diagnostics than deterministic methods. Beyond improved accuracy, the Bayesian framework enhances decision-making by explicitly representing uncertainty in building performance parameters. This provides a more realistic foundation for investment prioritization, demand forecasting, and long-term energy planning. The method is readily applicable to other building typologies or geographies, offering a scalable tool for data-driven energy management under uncertainty.

翻译：本研究提出了一种可扩展的贝叶斯建模框架，利用来自2,788栋丹麦独户住宅的智能电表数据评估建筑能耗性能。该框架结合贝叶斯统计推断与能量特征模型，以表征建筑的热工性能。该方法量化了热损失系数、太阳得热和风渗透等关键参数，同时提供完整的后验分布以反映参数不确定性。研究开发了三种模型变体：基准ES模型、考虑热惯性的自回归模型（ARX-ES），以及近似随机灰箱动力学的自回归移动平均模型（ARMAX-ES）。结果表明，模型复杂度提升了一步超前预测性能，其中ARMAX-ES模型在整个建筑群中实现了中位数贝叶斯R^2达0.94。在单体建筑层面，贝叶斯方法可为年度能耗需求提供$\pm1\%$内的可信区间，相比确定性方法具有更强的诊断鲁棒性。除提升精度外，该贝叶斯框架通过显式表征建筑性能参数的不确定性，增强了决策支持能力。这为投资优先级排序、需求预测和长期能源规划提供了更现实的基础。该方法可直接适用于其他建筑类型或地理区域，为不确定条件下的数据驱动能源管理提供了可扩展工具。