합성곱 신경망을 이용한 건물 온도 분포 및 열 손실 예측

Abstract

In this study, deep learning method was developed to predict temperature distribution and thermal loss of building considering thermal bridge. Previous studies developed a various kind of analytical method to evaluate energy performance of buildings. However, discontinuity of structures and thermal properties difference of building materials cause huge amount of thermal loss due to thermal bridge, and it was difficult to be predicted. Therefore, finite element method has been developed by previous researchers [1]. However, it hasstill limitation that simulation model should be made in every case considering its complex structures and thermal properties, which have a wide range. In addition, expert knowledge and technique for simulation is required for accurate prediction with high reliability. In this study, FEM database with a different geometries and material properties was constructed and trained by deep neural network based on convolutional neural network. With the pre-trained deep neural network, we can obtain the temperature distribution and total thermal loss considering thermal bridge in a few seconds by only entering blueprint and thermal properties. It is expected that the developed model can be used for various applications to train finite element analysis.