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BROWSE대한건축학회 논문집(구조계)2018-06 > DETAIL
  LITERATURE >JOURNALS [358868] Download the full text :   
Title   점진적 샘플링과 정규 상호정보량을 이용한 온라인 기계학습 공조기 급기온도 예측 모델 개발 / Development of Online Machine Learning Model for AHU Supply Air Temperature Prediction using Progressive Sampling and Normalized Mutual Information
Authors   추한경(Chu, Han-Gyeong) ; 신한솔(Shin, Han-Sol) ; 안기언(Ahn, Ki-Uhn) ; 라선중(Ra, Seon-Jung) ; 박철수(Park, Cheol Soo)
Organization   대한건축학회
Source   대한건축학회논문집 구조계, Vol.34 No.06(2018-06)
Page   Start Page(63) Total Page(7)
ISSN   1226-9107
Classification   재료 / 환경 및 설비 
Keywords   정규 상호정보량 ; 온라인 기계학습 모델 ; 점진적 샘플링 ; BEMS ; 정보 엔트로피//normalized mutual information ; online machine learning model ; progressive sampling ; Building Energy Management System ; information entropy
Abstract2   The machine learning model can capture the dynamics of building systems with less inputs than the first principle based simulation model. The training data for developing a machine learning model are usually selected in a heuristic manner. In this study, the authors developed a machine learning model which can describe supply air temperature from an AHU in a real office building. For rational reduction of the training data, the progressive sampling method was used. It is found that even though the progressive sampling requires far less training data (n=60) than the offline regular sampling (n=1,799), the MBEs of both models are similar (2.6% vs. 5.4%). In addition, for the update of the machine learning model, the normalized mutual information (NMI) was applied. If the NMI between the simulation output and the measured data is less than 0.2, the model has to be updated. By the use of the NMI, the model can perform better prediction (5.4% → 1.3%).
Location   대한건축학회