Evaluation of the Alberta air infiltration model using measurements and inter-model comparisons
Evaluation of the Alberta air infiltration model using measurements and inter-model comparisons
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DOI：
10.1016/j.buildenv.2008.03.005
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发表时间：
2009-02-01
影响因子：
7.4
通讯作者：
Reardon, James
中科院分区：
文献类型：
--
作者：
Wang, Weimin;Beausoleil-Morrison, Ian;Reardon, James
阿尔伯塔空气渗透模型（AIM-2）是一个简化的单区模型，用于预测具有许多显著特征的建筑物空气渗透率。本文提出了一个实证研究，这个模型使用的测量数据，从16个独立的住宅在加拿大渥太华。首先对每个房屋进行单风机降压试验，以确定其泄漏特性。然后，示踪气体浓度衰减技术被用来测量在广泛的天气条件下的空气渗透率。AIM-2模型被用来预测空气渗透率为每16所房子的测量天气条件。然后将这些模型预测与示踪气体试验确定的空气渗透率进行比较。此外，AIM-2模型的预测与另一个模型，劳伦斯伯克利实验室（LBL）模型进行了比较。AIM-2模型倾向于低估空气渗透率，但表现优于LBL模型。AIM-2模型的平均误差为19%，而LBL模型的误差为25%。AIM-2模型需要估计房屋的渗漏分布，这可能导致了一些分歧。尝试使用遗传算法，以减少估计这些模型的输入所造成的不确定性。(C)2008爱思唯尔有限公司保留所有权利。
The Alberta air infiltration model (AIM-2) is a simplified single-zone model for predicting building air infiltration rates with a number of salient features. This paper presents an empirical study of this model using measured data from 16 detached houses in Ottawa, Canada. A single-fan depressurization test was first conducted for each house to determine its leakage characteristics. Then, the tracer gas concentration decay technique was employed to measure air infiltration rates under a wide range of weather conditions. The AIM-2 model was used to predict air infiltration rates for each of the 16 houses for the measured weather conditions. These model predictions were then compared with the air infiltration rates determined with the tracer gas tests. Additionally, the predictions of the AIM-2 model were compared with those of another model, the Lawrence Berkeley Laboratory (LBL) model. The AIM-2 model tended to underestimate air infiltration rates but performed better than the LBL model. On average, the AIM-2 model has an error of 19% while the LBL model has an error of 25%. The AIM-2 model requires an estimation of the house's leakage distribution, which may have contributed to some of this disagreement. An attempt was made to use genetic algorithms to reduce the uncertainty caused by estimating these model inputs. (C) 2008 Elsevier Ltd. All rights reserved.