Predicting the hygrothermal behaviour of building components using neural networks

Abstract

Increasing the energy efficiency of the existing building stock can be accomplished by adding thermal insulation to the building envelope. In case of historic buildings with massive walls, internal insulation is often the only feasible post-insulation technique. Drawback of internal insulation is the modified hygrothermal response of the wall, which can result in moisture damage. Hence, it is crucial to assess the risk of damage accurately beforehand. Given the many uncertainties involved, a probabilistic assessment is advisable. This, however, would require thousands of simulations, which easily becomes computationally inhibitive. To overcome this time-efficiency issue, this paper proposes the use of neural networks to replace the original hygrothermal model. The neural network is trained on a small data set obtained from the hygrothermal model and can subsequently be used to predict the hygrothermal behaviour of building components with different boundary conditions and geometry. The transient nature of the hygrothermal behaviour requires a neural network type which can handle long-range time-dependencies. In the past, recurrent neural networks were often used for this type of data. Recently however, results indicate that convolutional neural networks can outperform recurrent neural networks on such tasks. This paper compares the prediction accuracy and training time of both neural network types for the prediction of the hygrothermal behaviour of building components.