Energy and carbon performance of urban buildings using metamodeling variable importance techniques
Energy and carbon performance of urban buildings using metamodeling variable importance techniques
复制标题
使用元建模变量重要性技术的城市建筑的能源和碳性能
DOI：
10.1007/s12273-020-0688-0
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发表时间：
2020-09
影响因子：
5.5
通讯作者：
Zhou Xiang
中科院分区：
文献类型：
--
作者：
Liu Yunliang;Tian Wei;Zhou Xiang
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点之一。变量重要性技术已广泛应用于建筑能源分析，以确定关键影响因素。
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.
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影响因子：
5.5
作者：
David Muñoz;G. Besuievsky;G. Patow
通讯作者：
David Muñoz;G. Besuievsky;G. Patow
影响因子：
2.5
作者：
E. Ziegel
通讯作者：
E. Ziegel
DOI：
--
发表时间：
2007
期刊：
--
影响因子：
--
作者：
Fangting Song;Q. Zhu;Ruhong Wu;Yi Jiang;A. Xiong;Bomin Wang;Yanjun Zhu;Qingxiang Li
通讯作者：
Fangting Song;Q. Zhu;Ruhong Wu;Yi Jiang;A. Xiong;Bomin Wang;Yanjun Zhu;Qingxiang Li
影响因子：
6.7
作者：
A. Mastrucci;P. Pérez‐López;E. Benetto;U. Leopold;I. Blanc
通讯作者：
A. Mastrucci;P. Pérez‐López;E. Benetto;U. Leopold;I. Blanc
影响因子：
9
作者：
Caputo, Paola;Costa, Gaia;Ferrari, Simone
通讯作者：
Ferrari, Simone