E3S Web Conf.
Volume 172, 202012th Nordic Symposium on Building Physics (NSB 2020)
|Number of page(s)||6|
|Published online||30 June 2020|
Using convolutional neural networks for hygrothermal predictions to extrapolate to other external climates
KU Leuven, Department of Civil Engineering, Building Physics Section, Kasteelpark Arenberg 40 Bus 2447, 3001 Heverlee, Belgium
* Corresponding author: firstname.lastname@example.org
When simulating the hygrothermal behaviour of a building component, many uncertainties are involved (e.g. exterior and interior climates, material properties, configuration geometry). In contrast to a deterministic assessment, a probabilistic analysis enables including these uncertainties, and thus allows a more reliable assessment of the hygrothermal performance. This easily involves thousands of simulations, which easily becomes computationally inhibitive. To overcome this time-efficiency issue, a convolutional neural network, a type of metamodel mimicking the original model with a strongly reduced calculation time, can replace the hygrothermal model. This was proven in a previous study for a massive masonry wall, where variability of exterior and interior climate, brick material properties and wall geometry was included. However, the question rises whether it is possible to train the network on a limited number of climates, and afterwards use the network to predict accurately for other climates as well. This paper thus focuses on this aspect, and results show that, as long as the range of the new climate data falls within the range of the climate data the network was trained on, the network is able to predict accurately for new climates as well.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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