AI 中文总结
针对建筑节能不确定性估计问题，回顾多种方法，改进贝叶斯建模方法，通过大规模数值测试，结果表明贝叶斯方法能给出一致估计，为估计含自相关残差的非线性回归模型不确定性提供有效方法。
AI 中文摘要
在建筑能源效率领域，能效措施后的节能测量与验证（M&V）常依赖校准统计模型。为获可靠估计，与该过程相关的不确定性估计是M&V的关键方面。文献中已提出多种计算不确定性的方法，但近期工作对其准确性提出严重质疑，尤其是对于具有精细时间分辨率（每小时和每日）的能源数据，自相关问题更为重要。本文针对每日数据聚焦此问题，有三个主要贡献：一是详细全面回顾文献中针对线性和非线性回归模型提出的几种方法；二是改进基于贝叶斯建模的具有自相关残差的非线性回归模型方法；三是基于包含数千次随机热动态模拟的合成数据集对不同方法进行大规模数值测试。结果表明在受控数值环境中可测试不同方法的准确性，特别是贝叶斯方法能给出一致的不确定性估计，为估计M&V中常遇到的具有自相关残差的非线性回归模型的不确定性提供了有说服力的方法。
英文摘要
In the field of building energy efficiency, the measurement and verification (M&V) of energy savings following energy efficiency measures often relies on the use of a calibrated statistical model. In order to obtain reliable estimates, the estimation of uncertainties associated with this procedure is recognized as a crucial aspect of M&V. Several approaches have been proposed in the literature to compute uncertainties but a recent work has raised serious doubts on their accuracy. This is especially the case for energy data with fine time resolution (hourly and daily), for which the issues associated with autocorrelations become more important. This work focuses on this issue for daily data with three main contributions: (i) a detailed comprehensive review of several methods proposed in the literature for linear and non-linear regression models (including exact formulas for linear models, the ASHRAE 14 approximate formula and resampling approaches) ; (ii) the improvement of approaches based on Bayesian modeling for non-linear regression models with autocorrelated residuals ; (iii) a large scale numerical test of the different approaches based on a synthetic dataset containing thousands of stochastic thermal dynamic simulations. Our results permit to test the accuracy of different approaches in a controlled numerical setting. In contrast with previous work, we find in particular that our Bayesian approach leads on our dataset to consistent estimates of uncertainties. This suggests that the proposed Bayesian approach offers a convincing method to estimate uncertainties in non-linear regression models with autocorrelated residuals often encountered M&V.
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