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首页> 外文期刊>International Journal of Refrigeration >Modelling of a thermal insulation system based on the coldest temperature conditions by using artificial neural networks to determine performance of building for wall types in Turkey
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Modelling of a thermal insulation system based on the coldest temperature conditions by using artificial neural networks to determine performance of building for wall types in Turkey

机译：基于最冷温度条件的保温系统建模，方法是使用人工神经网络确定土耳其墙体类型的建筑性能

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摘要

In formation of building external envelope, as two important criteria, climatic data and wall types must be taken into consideration. In the selection of wall type, the thickness of thermal insulation layer (d_i) must be calculated. As a new approach, this study proposes determining the thermal insulation layer by using artificial neural network (ANN) technique. In this technique five different wall types in four different climatic regions in Turkey have been selected. The ANN was trained and tested by using MATLAB toolbox on a personal computer. As ANN input parameters, U_w, T_(e,Met), T_(e,TSE), R_(wt), and q_(TSE) were used, while d_i was the output parameter. It was found that the maximum mean absolute percentage error (MRE,%) is less than 7.658%. R~2 (%) for the training data were found ranging about from 99.68 to 99.98 and R~2; for the testing data varied between 97.55 and 99.96. These results show that ANN model can be used as a reliable modeling method of d_i studies.
机译：在形成建筑物外部围护结构时，必须考虑气候数据和墙体类型这两个重要标准。在选择墙类型时，必须计算出隔热层的厚度（d_i）。作为一种新方法，本研究提出使用人工神经网络（ANN）技术确定隔热层。在这项技术中，已选择了土耳其四个不同气候区域中的五种不同墙类型。通过在个人计算机上使用MATLAB工具箱对ANN进行了培训和测试。作为ANN输入参数，使用了U_w，T_（e，Met），T_（e，TSE），R_（wt）和q_（TSE），而d_i是输出参数。发现最大平均绝对百分比误差（MRE，％）小于7.658％。训练数据的R〜2（％）约为99.68〜99.98，R〜2;测试数据在97.55和99.96之间变化。这些结果表明，人工神经网络模型可以用作d_i研究的可靠建模方法。

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