Impact Assessment in the Process of Propagating Climate Change Uncertainties into Building Energy Use
Impact Assessment in the Process of Propagating Climate Change Uncertainties into Building Energy Use
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DOI：
10.3390/en14020367
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发表时间：
2021-01
期刊：
影响因子：
3.2
通讯作者：
Hamed Yassaghi;S. Hoque
中科院分区：
文献类型：
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作者：
Hamed Yassaghi;S. Hoque
由于气候变化，建筑物会受到巨大的压力，气候适应性建筑的设计策略充满了不确定性，这可能使解释能源使用分布变得困难和可疑。本研究旨在增强对气候变化下建筑能源性能的不确定性和解释的可靠性和可靠性。一个四步的气候不确定性传播方法，传播到建筑物的能源使用缩小未来的天气文件的不确定性进行检查。这四步方法集成了动态建筑模拟、拟合年均天气变量分布、回归模型（年均天气变量与能源使用之间）和随机抽样。将不同分布拟合到天气变量（如正态分布、Beta分布、Weibull分布等）的影响并以一个参考办公楼为例，对不确定度传播法的回归模型（多元线性回归和主成分回归）进行了评价。结果表明，选择一个完整的主成分回归模型的天气变量的每个主成分的最佳拟合分布，可以减少输出能量分布的变化相比，模拟数据。研究结果提供了一种理解复合建筑能源使用分布和解析气候预测的不确定性的方法。
Buildings are subject to significant stresses due to climate change and design strategies for climate resilient buildings are rife with uncertainties which could make interpreting energy use distributions difficult and questionable. This study intends to enhance a robust and credible estimate of the uncertainties and interpretations of building energy performance under climate change. A four-step climate uncertainty propagation approach which propagates downscaled future weather file uncertainties into building energy use is examined. The four-step approach integrates dynamic building simulation, fitting a distribution to average annual weather variables, regression model (between average annual weather variables and energy use) and random sampling. The impact of fitting different distributions to the weather variable (such as Normal, Beta, Weibull, etc.) and regression models (Multiple Linear and Principal Component Regression) of the uncertainty propagation method on cooling and heating energy use distribution for a sample reference office building is evaluated. Results show selecting a full principal component regression model following a best-fit distribution for each principal component of the weather variables can reduce the variation of the output energy distribution compared to simulated data. The results offer a way of understanding compound building energy use distributions and parsing the uncertain nature of climate projections.