Global sensitivity analysis as a support for the generation of simplified building stock energy models
Global sensitivity analysis as a support for the generation of simplified building stock energy models
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DOI：
10.1016/j.enbuild.2017.05.022
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发表时间：
2017-08
影响因子：
6.7
通讯作者：
A. Mastrucci;P. Pérez‐López;E. Benetto;U. Leopold;I. Blanc
中科院分区：
文献类型：
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作者：
A. Mastrucci;P. Pérez‐López;E. Benetto;U. Leopold;I. Blanc
建筑物占欧洲最终能源消耗总量的40%。最近开发了许多自下而上的模型，以支持地方当局评估大型建筑物的能源消耗和减排潜力。然而，目前的模型很少考虑与建筑物的使用和特性的不确定性内的股票，从而导致潜在的偏见results.This研究提出了一个通用的模型简化方法，使用不确定性传播和随机灵敏度分析，以获得快速简化（代理）模型来估计当前的建筑存量能源使用，以改善城市规划。该方法包括一个基于工程的能源模型作为输入的全球敏感性分析（GSA）使用的基本效应（EE）筛选和Sobol'方法的关键参数识别和回归分析，以推导出简化的模型，为整个建筑存量。（卢森堡）表明，解释供暖和家用热水最终能源使用的大部分可变性的参数是地板面积、设定点温度、外墙U值、窗户和加热系统类型。简化模型的结果进行了验证，对实测数据，并确认了一个简单而强大的评估的建筑存量能源使用考虑不确定性和可变性的方法的有效性。
Buildings are responsible for 40% of total final energy consumptions in Europe. Numerous bottom-up models were recently developed to support local authorities in assessing the energy consumption of large building stocks and reduction potentials. However, current models rarely consider uncertainty associated to building usage and characteristics within the stock, resulting in potentially biased results.This study presents a generic model simplification approach using uncertainty propagation and stochastic sensitivity analysis to derive fast simplified (surrogate) models to estimate the current building stock energy use for improved urban planning. The methodology includes an engineering-based energy model as input to global sensitivity analysis (GSA) using the elementary effects (EE) screening and Sobol’ method for key parameters identification and regression analysis to derive simplified models for entire building stocks.The application to the housing stock of Esch-sur-Alzette (Luxembourg) showed that the parameters explaining most of the variability in final energy use for heating and domestic hot water are floor area, set-point temperature, external walls U-values, windows and heating system type. Results of the simplified models were validated against measured data and confirmed the validity of the approach for a simple yet robust assessment of the building stock energy use considering uncertainty and variability.