| id |
ecaade2024_101 |
| authors |
Yu, Jiaqi; Guo, Kening; Bai, Zishen; Wen, Zitong |
| year |
2024 |
| title |
Application of Artificial Neural Network for Predicting U-Values of Building Envelopes in Temperate Zones |
| source |
Kontovourkis, O, Phocas, MC and Wurzer, G (eds.), Data-Driven Intelligence - Proceedings of the 42nd Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2024), Nicosia, 11-13 September 2024, Volume 1, pp. 585–592 |
| doi |
https://doi.org/10.52842/conf.ecaade.2024.1.585
|
| summary |
Due to the global energy deficit, building energy consumption has become a significant issue in recent years. Many researchers have focused on building energy consumption simulations to manage energy consumption accurately and provide a comfortable indoor environment for occupants. In building energy simulations, accurate input of building parameters is essential. As important thermal parameters, the thermal transmittance (U-value) of building envelopes can affect building operational energy consumption. In most building energy simulation studies, the U-value was set to the theoretical U-value which was a fixed value. However, the U-value constantly varies due to several environmental impacts, especially fluctuating air temperature and relative humidity (T/RH). Thus, the U-values are dynamic in actual situations, and inputting dynamic U-values into building energy simulations can reduce the gap between the simulation and the actual situation. In this study, the dynamic U-values of conventional cavity envelopes in temperate zones were predicted by an artificial neural network (ANN) model. Firstly, the in-situ dynamic U-value measurement was conducted in Sheffield, the UK, from summer to winter in 2022. The heat flow meter method was applied, and the tested envelope was a conventional cavity envelope widely used in the UK. The indoor and outdoor T/RH were measured and recorded as well. Then, the measured data were applied to train the optimal ANN model. The input parameters included the indoor and outdoor T/RH, and the output parameter was the dynamic U-value. Finally, the prediction results obtained by the optimal ANN model were closely correlated with the measured dynamic U-value. This quantitative study of dynamic U-values examined the relationship between dynamic U-values of conventional cavity envelopes and environmental factors, which can provide reliable information for improving the inputting patterns of building parameters and the accuracy of the building energy simulation. |
| keywords |
Artificial Neural Network Model, In-situ U-value Measurement, Dynamic U-value Prediction, Conventional Cavity Envelopes |
| series |
eCAADe |
| email |
|
| full text |
 file.pdf (1,919,915 bytes) |
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| last changed |
2024/11/17 22:05 |
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