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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Astrid Tijskens & Hans Janssen & Staf Roels, 2019. "Optimising Convolutional Neural Networks to Predict the Hygrothermal Performance of Building Components," Energies, MDPI, vol. 12(20), pages 1-18, October.
  • Handle: RePEc:gam:jeners:v:12:y:2019:i:20:p:3966-:d:277997
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    References listed on IDEAS

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    1. Janssen, Hans, 2013. "Monte-Carlo based uncertainty analysis: Sampling efficiency and sampling convergence," Reliability Engineering and System Safety, Elsevier, vol. 109(C), pages 123-132.
    2. Bienvenido-Huertas, David & Moyano, Juan & Rodríguez-Jiménez, Carlos E. & Marín, David, 2019. "Applying an artificial neural network to assess thermal transmittance in walls by means of the thermometric method," Applied Energy, Elsevier, vol. 233, pages 1-14.
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    1. Mikael Salonvaara & Andre Desjarlais & Antonio J. Aldykiewicz & Emishaw Iffa & Philip Boudreaux & Jin Dong & Boming Liu & Gina Accawi & Diana Hun & Eric Werling & Sven Mumme, 2023. "Application of Machine Learning to Assist a Moisture Durability Tool," Energies, MDPI, vol. 16(4), pages 1-20, February.

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