Characterization of an airflow network model by sensitivity analysis: parameter screening, fixing, prioritizing and mapping
Characterization of an airflow network model by sensitivity analysis: parameter screening, fixing, prioritizing and mapping
复制标题
DOI：
10.1080/19401493.2015.1110621
复制
发表时间：
2017-01
影响因子：
2.5
通讯作者：
F. Monari;P. Strachan
中科院分区：
文献类型：
--
作者：
F. Monari;P. Strachan
由于详细的热模拟程序中的模型过度参数化，进行验证或校准研究的模型师在将模型输出与现场测量进行比较的情况下，在确定那些主要导致观测差异的参数时面临困难。在进行敏感性研究时，通常使用Morris方法来确定最有影响的参数。它们通常伴随着使用蒙特卡洛模拟的不确定性分析，以产生预测周围的置信度。提出了一种基于全局灵敏度分析方法的更为严密的灵敏度分析方法，该方法分为三个阶段：因子筛选、因子排序与确定、因子映射。将该方法应用于IEA ECB附录58中获得的一个详细的经验验证数据集，重点研究了气流网络，这是一个输入具有很大不确定性的模拟程序子模型。
Due to the over-parameterized models in detailed thermal simulation programs, modellers undertaking validation or calibration studies, where the model output is compared against field measurements, face difficulties in determining those parameters which are primarily responsible for observed differences. Where sensitivity studies are undertaken, the Morris method is commonly applied to identify the most influential parameters. They are often accompanied by uncertainty analysis using Monte Carlo simulations to generate confidence bounds around the predictions. This paper sets out a more rigorous approach to sensitivity analysis (SA) based on a global SA method with three stages: factor screening, factor prioritizing and fixing, and factor mapping. The method is applied to a detailed empirical validation data set obtained within IEA ECB Annex 58, with the focus of the study on the airflow network, a simulation program sub-model which is subject to large uncertainties in its inputs.