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

Architectural Institute of Korea (대한건축학회)
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Development of a Machine Learning Model for a Chiller using Random Forest Algorithm and Data Pre-processing

랜덤 포레스트와 데이터 전처리를 이용한 냉동기 기계학습 모델 개발

  • Received : 2017.04.24
  • Accepted : 2017.07.24
  • Published : 2017.09.30
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Abstract

It has been widely acknowledged that a machine learning model can be used as a surrogate to a first-principle based dynamic simulation model. The accuracy and computation efficiency of a machine learning model is dependent on a combination of input variables. The random forest algorithm, one of the machine learning algorithms, can calculate a variable importance that determines the influence of each input variable on the output of the model. In this study, the authors developed three random forest models of a chiller in an existing building as follows: (1) Model A consisting of 12 measured variables from BEMS data, (2) Model B consisting of 2 measured input variables plus 4 new variables constructed by random selection, and (3) Model C consisting of 4 measured input variables plus 2 new variables constructed based on a physics-based equation. The CVRMSE of the three models are 8.56%, 5.44%, and 4.28%, respectively. The findings of this study can be summarized threefold: (1) all three random forest models are good enough to describe the dynamics of the chiller system, (2) the random forest machine learning algorithm can be used to develop a simulation model of the system, and (3) an accurate model can be constructed either by the random selection or the physics-based equation, even when a few input variables are given.

Keywords

Acknowledgement

Supported by : 한국에너지기술평가원(KETEP)

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