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Machine Learned Façade
- Subtitle
- Experimental Investigation of AI Model Performance for Daylight Control Using XAI
- Alternative Title
- 머신-런드 파사드: XAI를 이용한 일조조절을 위한 AI모델 성능 실험 분석
- Author(s)
- MOLLAEIUBLI TAKHMASIB
- Advisor
- Hwang Yi
- Department
- 일반대학원 스마트융합건축학과
- Publisher
- The Graduate School, Ajou University
- Publication Year
- 2023-08
- Language
- eng
- Alternative Abstract
- The façade of a building critically influences occupant visual and thermal comfort interfacing between the indoor and the external environment, and dynamic transformation of the façade can improve the building’s performance significantly. This study addresses the use of artificial intelligence (AI) to operate climate-responsive kinetic façade (KF) systems. The KF is a dynamic and morphologically responsive mechanism designed to improve environmental building performance (EBP). Despite recent design advancements and emerging material approaches in the KF building studies, intelligent strategies of morphological operation have been little investigated. To examine our primary hypothesis that optimal undulation with open and close of the adaptive KF controlled by AI models effectively improves indoor daylight glare and illumination performance, we selected three machine-learning (ML) regressors (Extreme Gradient Boosting (XGBoost), Random Forest (RFR), Decision Tree (DTR). The ML models were trained and tested with a set of 20,000 data points from EBP simulation using Radiance on Rhino Grasshopper. KF modules were parametrically designed and fully fabricated (width = 1.73m, height = 1.1m). For demonstration, the KF design and AI control were implemented in a test mockup (floor length = 3m, width = 2m) in Suwon, South Korea. The façade shape was optimized according to indoor daylight probability (DGP) and daylight, respectively in real-time, by using a differential evolution algorithm. Sensor data were collected in dynamic operation. Results indicated that tree-based algorithms outperformed other ML algorithms, achieving more than R2 of 0.85 – 0.90 accuracy compared to neural networks’ R2 values of 0.5 accuracies in predicting DGP and illuminance. Furthermore, we observed that training of a full-scale façade module was highly dependent on the amount of dataset and hyperparameter combinations, and the accuracy of the RFR depended on the maximum depth.
- Fulltext
- Appears in Collections:
- Graduate School of Ajou University > Department of Smart Convergence Architecture > 3. Theses(Master)
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