Conference Editor
Jianshun Zhang; Edward Bogucz; Cliff Davidson; Elizabeth Krietmeyer
Keywords:
Internal insulation, hygrothermal performance, metamodel, neural networks, LSTM
Location
Syracuse, NY
Event Website
http://ibpc2018.org/
Start Date
25-9-2018 1:30 PM
End Date
25-9-2018 3:00 PM
Description
In recent years, probabilistic assessment of hygrothermal performance of building components has received increasing attention. Given the many uncertainties involved in the hygrothermal behaviour of building components, a probabilistic assessment enables to assess the damage risk more reliably. However, this typically involves thousands of simulations, which easily becomes computationally inhibitive. To overcome this time-efficiency issue, this paper proposes the use of much faster metamodels. This paper focusses on neural networks, as they have proven to be successful in other non-linear and non-stationary research applications. Two types of networks are considered: the traditional multilayer perceptron (with and without a time window) and memory neural networks (LSTM, GRU). Both are used for predicting the hygrothermal behaviour of a massive wall. The results showed that all networks are capable to predict the temperature profiles accurately, but only the LSTM and GRU networks could predict the slow responses of relative humidity and moisture content. Furthermore, the LSTM and GRU network were found to have almost equal predicting accuracy, though the GRU converged faster
Recommended Citation
Tijskens, Astrid; Roels, Staf; and Janssen, Hans, "Neural networks to predict the hygrothermal response of building components in a probabilistic framework" (2018). International Building Physics Conference 2018. 4.
DOI
https://doi.org/10.14305/ibpc.2018.ms-6.04
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.
Neural networks to predict the hygrothermal response of building components in a probabilistic framework
Syracuse, NY
In recent years, probabilistic assessment of hygrothermal performance of building components has received increasing attention. Given the many uncertainties involved in the hygrothermal behaviour of building components, a probabilistic assessment enables to assess the damage risk more reliably. However, this typically involves thousands of simulations, which easily becomes computationally inhibitive. To overcome this time-efficiency issue, this paper proposes the use of much faster metamodels. This paper focusses on neural networks, as they have proven to be successful in other non-linear and non-stationary research applications. Two types of networks are considered: the traditional multilayer perceptron (with and without a time window) and memory neural networks (LSTM, GRU). Both are used for predicting the hygrothermal behaviour of a massive wall. The results showed that all networks are capable to predict the temperature profiles accurately, but only the LSTM and GRU networks could predict the slow responses of relative humidity and moisture content. Furthermore, the LSTM and GRU network were found to have almost equal predicting accuracy, though the GRU converged faster
https://surface.syr.edu/ibpc/2018/MS6/4
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