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Validation of a model for predicting airtightness of residential units

机译：验证预测住宅单位气密性的模型

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Airtightness plays a significant role in buildings energy efficiency. This paper describes validation of the new model for predicting airtightness values of residential units. This model utilizes a neural network in prediction of airtightness and is obtained based on in situ measurements at 58 units in the local Osijek area (Croatia) carried out during 2013. The model presents new approach to airtightness estimations by using 4 corrective factors associated with building envelope elements and their airtightness properties. The model was validated in local field conditions, but independent validation of the model in this paper was made on 5 residential buildings in the Republic of Serbia in order to determine its applicability on new data set outside local area of Osijek. The proposed model requires reduced amount of data for predicting airtightness values of residential units and therefore is faster and more economical than the actual field measurements. The proposed model could also be used for predicting airtightness values at the initial design phase already, as well as for planning systematic energy refurbishment of residential buildings in order to achieve adequate energy efficiency and appropriate thermal comfort in accordance with EU recommendations in this field.
机译：气密性在建筑物能源效率中起着重要作用。本文介绍了预测住宅单位的气密价值的新模型的验证。该模型利用神经网络预测气密性，并且基于在2013年实施的本地奥西克地区（克罗地亚）的58个单元的原位测量获得。该模型通过使用与建筑物相关的4个矫正因素来提出新的气密估计方法包络元素及其气密性能。该模型在本地现场条件下验证，但本文的独立验证是在塞尔维亚共和国的5个住宅建筑物上制作的，以确定其对奥西克地区外部的新数据的适用性。所提出的模型需要减少用于预测住宅单元的气密值的数据量，因此比实际的场测量更快，更经济。所提出的模型还可以用于预测初始设计阶段的气密值，以及计划居民建筑的系统能源翻新，以便根据该领域的欧盟建议，实现足够的能效和适当的热舒适度。

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