Journal of the Architectural Institute of Korea Structure & Construction (대한건축학회논문집:구조계)

Architectural Institute of Korea (대한건축학회)
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Development of Online Machine Learning Model for AHU Supply Air Temperature Prediction using Progressive Sampling and Normalized Mutual Information

점진적 샘플링과 정규 상호정보량을 이용한 온라인 기계학습 공조기 급기온도 예측 모델 개발

  • 추한경 (성균관대 건설환경시스템공학과) ;
  • 신한솔 (성균관대 건설환경시스템공학과) ;
  • 안기언 (성균관대 건설환경시스템공학과) ;
  • 라선중 (성균관대 미래도시융합공학과) ;
  • 박철수 (서울대학교 건축학과.서울대학교 건설환경종합연구소)
  • Received : 2018.04.18
  • Accepted : 2018.06.18
  • Published : 2018.06.30
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Abstract

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 ($5.4%{\rightarrow}1.3%$).

Keywords

Acknowledgement

Supported by : 한국에너지기술평가원 (KETEP)

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