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

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 (di) 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, Uw, Te,Met, Te,TSE, Rwt, and qTSE were used, while di was the output parameter. It was found that the maximum mean absolute percentage error (MRE, %) is less than 7.658%. R2 (%) for the training data were found ranging about from 99.68 to 99.98 and R2 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 di studies.