| id |
caadria2023_412 |
| authors |
Li, Yuanyuan, Huang, Chenyu and Yao, Jiawei |
| year |
2023 |
| title |
Optimising the Control Strategies for Performance-Driven Dynamic Building Facades Using Machine Learning |
| doi |
https://doi.org/10.52842/conf.caadria.2023.1.199
|
| source |
Immanuel Koh, Dagmar Reinhardt, Mohammed Makki, Mona Khakhar, Nic Bao (eds.), HUMAN-CENTRIC - Proceedings of the 28th CAADRIA Conference, Ahmedabad, 18-24 March 2023, pp. 199–208 |
| summary |
The balance between energy consumption and indoor environmental comfort is a continuing research topic in building energy efficiency. The dynamic façades (DF) are considered a practical approach to separate the sun and create more shadows for buildings with curtain walls, reducing the HVAC system's energy consumption. However, the design complexity of the DF leads to a time-consuming simulation process, making it difficult to modify the design parameters in the early design stage efficiently. This paper provides optimized control strategies for four dynamic façade prototypes. We use explainable machine learning to explore the relationship between design parameters of DF and indoor performance, including Energy Use Intensity (EUI) and Daylight Glare Probability (DGP). We deployed the trained model in optimizing the rotation angle of DF per hour on a typical day to minimize the EUI and DGP of the target room. The results show that the rotation angle of DF significantly affects the DGP, whereas the room size affects EUI performance more than rotation angles. Optimized control strategies of DF bring a maxim 13.5% EUI decrease and 51.7% reduction of DGP. Our work provides a generalizable design flow for performance-driven dynamic skin design. |
| keywords |
Dynamic façade, Energy consumption, Indoor comfort, computational simulation, Multi-objective optimization, Machine learning |
| series |
CAADRIA |
| email |
|
| full text |
 file.pdf (10,783,986 bytes) |
| references |
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| last changed |
2023/06/15 23:14 |
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