Global urbanization causes more environmental stresses in cities and energy efficiency is one of major concerns for urban sustainability. The variable importance techniques have been widely used in building energy analysis to determine key factors influencing building energy use. Most of these applications, however, use only one type of variable importance approaches. Therefore, this paper proposes a procedure of conducting two types of variable importance analysis (predictive and variance-based) to determine robust and effective energy saving measures in urban buildings. These two variable importance methods belong to metamodeling techniques, which can significantly reduce computational cost of building energy simulation models for urban buildings. The predictive importance analysis is based on the prediction errors of metamodels to obtain importance rankings of inputs, while the variance-based variable importance can explore non-linear effects and interactions among input variables based on variance decomposition. The campus buildings are used to demonstrate the application of the method proposed to explore characteristic of heating energy, cooling energy, electricity, and carbon emissions of buildings. The results indicate that the combination of two types of metamodeling variable importance analysis can provide fast and robust analysis to improve energy efficiency of urban buildings. The carbon emissions can be reduced approximately 30% after using a few of effective energy efficiency measures and more aggressive measures can lead to the 60% of reduction of carbon emissions. Moreover, this research demonstrates the application of parallel computing to expedite building energy analysis in urban environment since more multi-core computers become increasingly available.

全球城市化给城市带来了更多的环境压力，能源效率是城市可持续发展的主要关注之一。重要性可变技术已广泛用于建筑能耗分析，以确定影响建筑能耗的关键因素。但是，大多数这些应用程序仅使用一种类型的可变重要性方法。因此，本文提出了一种进行两种类型的可变重要性分析（预测性和基于方差）的程序，以确定城市建筑中鲁棒而有效的节能措施。这两种重要程度可变的方法属于元建模技术，可以显着降低城市建筑物建筑能耗模拟模型的计算成本。预测重要性分析基于元模型的预测误差来获得输入的重要性排名，而基于方差的变量重要性可以基于方差分解探索输入变量之间的非线性影响和相互作用。校园建筑用于演示所提出方法的应用，以探索建筑的热能，冷能，电和碳排放的特征。结果表明，两种类型的元建模变量重要性分析的组合可以提供快速而可靠的分析，以提高城市建筑的能源效率。使用一些有效的能源效率措施后，碳排放量可减少约30％，而更积极的措施则可减少60％的碳排放量。此外，