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한국생활환경학회|한국생활환경학회지 2020년 8월

기사명 다기준 의사결정기법과 기계학습을 이용한 가변형 차양의 최적 형태 도출 / Optimal Design of a Dynamic Shading Device using Multi-Criteria Decision Making Methods and Machine Learning Algorithms
저자명 김랑 ; 김선호 ; 이예린 ; 문현준
발행사 한국생활환경학회
수록사항 한국생활환경학회지 , Vol.27 No.4(2020-08)
페이지 시작페이지(511) 총페이지(11)
ISSN 12261289
주제분류 환경및설비
주제어 ; External dynamic shading device; Multi-objective optimization; Random Forest; Genetic Algorithm; TOPSIS; MCDM(Multi-Criteria Decision Making)
요약2 This paper suggests a multi-objective optimization process for a dynamic shading device to minimize cooling load and maintain the required illuminance. We have developed a Random Forest model based on the data from IDA ICE simulation. A non-dominated sorting genetic algorithm (NSGA-II) is applied to optimize the extrude length of the upper 2-axis of the dynamic shading device at each hour. The developed Random Forest model could predict the cooling load and illuminance with appropriate accuracy (CVRMSE of 1.55% and 3.58%, respectively) compared to the ground truth from the IDA ICE simulation. The optimal shape of the shading device is determined among several alternatives using the TOPSIS method that is one of the multi-criteria decision making methods. In this process, two objectives (cooling load and illuminance) could have different weights to reflect the priority or preference of decisionmakers.
As a result, the optimal shape of the dynamic shading device derived from the multi-objective optimization process could provided improved performances both on cooling load and visual comfort. Besides, our process was able to consider different weights of each objective depending on the decision makers’ preferences.
소장처 한국생활환경학회
언어 한국어
DOI http://dx.doi.org/10.21086/ksles.2020.02.27.4.511