Optimising Convolutional Neural Networks to Predict the Hygrothermal Performance of Building Components

Astrid Tijskens, Hans Janssen and Staf Roels
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Astrid Tijskens: Department of Civil Engineering, KU Leuven, Building Physics Section, Kasteelpark Arenberg 40 Bus 2447, 3001 Heverlee, Belgium
Hans Janssen: Department of Civil Engineering, KU Leuven, Building Physics Section, Kasteelpark Arenberg 40 Bus 2447, 3001 Heverlee, Belgium
Staf Roels: Department of Civil Engineering, KU Leuven, Building Physics Section, Kasteelpark Arenberg 40 Bus 2447, 3001 Heverlee, Belgium

Energies, 2019, vol. 12, issue 20, 1-18

Abstract: Performing numerous simulations of a building component, for example to assess its hygrothermal performance with consideration of multiple uncertain input parameters, can easily become computationally inhibitive. To solve this issue, the hygrothermal model can be replaced by a metamodel, a much simpler mathematical model which mimics the original model with a strongly reduced calculation time. In this paper, convolutional neural networks predicting the hygrothermal time series (e.g., temperature, relative humidity, moisture content) are used to that aim. A strategy is presented to optimise the networks’ hyper-parameters, using the Grey-Wolf Optimiser algorithm. Based on this optimisation, some hyper-parameters were found to have a significant impact on the prediction performance, whereas others were less important. In this paper, this approach is applied to the hygrothermal response of a massive masonry wall, for which the prediction performance and the training time were evaluated. The outcomes show that, with well-tuned hyper-parameter settings, convolutional neural networks are able to capture the complex patterns of the hygrothermal response accurately and are thus well-suited to replace time-consuming standard hygrothermal models.

Keywords: Metamodeling; Convolutional neural networks; Time series modelling; Probabilistic assessment; Hygrothermal assessment (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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