Neural networks for metamodelling the hygrothermal behaviour of building components

September 2019 - Posted: 11/8/2019

A metamodel with a memory mechanism is required to accurately predict hygrothermal time series. Recurrent neural networks and dilated causal convolutional networks are able to capture the complex patterns of the hygrothermal response. To predict the relative humidity, dilated causal convolutional neural networks perform significantly better than recurrent neural networks. Dilated causal convolutional networks are 10 times faster to train on the current example, compared to recurrent neural networks.

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By: Astrid Tijskens, Staf Roels, Hans Janssen
Publisher: Building and Environment, Volume 162