한국정보전자통신기술학회논문지 (The Journal of Korea Institute of Information, Electronics, and Communication Technology)

  • 제14권5호
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  • Pages.413-420
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  • 2021
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  • 2005-081X(pISSN)
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  • 2288-9302(eISSN)
한국정보전자통신기술학회 (Korea Information Electronic Communication Technology)
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실내 환경 평가 시 미확보 파라미터 예측을 위한 기계학습 모델에 대한 연구

A Study on Machine Learning Model for Predicting Uncollected Parameters in Indoor Environment Evaluation

  • Jeong, Jin-Hyoung (Department of Biomedical IT, Catholic Kwandong University) ;
  • Jo, Jae-Hyun (Department of Bio-medical Engineering, Catholic Kwandong University) ;
  • Kim, Seung-Hun (Department of Bio-medical Engineering, Catholic Kwandong University) ;
  • Bang, So-Hyeon (Department of Biomedical IT, Catholic Kwandong University) ;
  • Lee, Sang-Sik (Department of Bio-medical Engineering, Catholic Kwandong University)
  • 투고 : 2021.10.05
  • 심사 : 2021.10.25
  • 발행 : 2021.10.30
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초록

본 연구는 수집 파라미터 중 하나가 부족할 경우 다른 파라미터를 통해 부족한 파라미터를 예측하기 위한 기계학습 모델에 대한 연구로서, 실내 환경 데이터 수집 장치를 통해 시간에 따른 온도·습도·CO2농도·광량에 대한 데이터를 수집하고, 수집한 데이터를 Matlab내 기계학습 회귀분석 기능을 통해 시간·온도·습도·CO2·광량 데이터를 예측하는 회귀모델을 만들었다. 또한 각 파라미터별로 RMSE 값이 가장 적은 3가지 모델을 선정하였으며 이에 대한 검증을 진행했다. 검증을 위해 각 파라미터로 도출된 예측모델에 테스트 데이터를 적용하여 예측치를 구했으며, 실측치와 구해진 예측치 간의 상관계수와 오차 평균을 구한 후 이를 비교하였다.

This study is about a machine learning model for predicting insufficient parameters through other parameters when one of the collected parameters is insufficient. A regression model was created to predict time, temperature, humidity, CO2, and light quantity data through the machine learning regression analysis function in Matlab. In addition, the three models with the lowest RMSE values for each parameter were selected and verified. For verification, the predicted values were obtained by applying the test data to the prediction model derived from each parameter, and the correlation coefficient and error average between the measured values and the obtained predicted values were obtained and then compared.

키워드

과제정보

Following are results of a study on the "Leaders in INdustry-university Cooperation +" Project, supported by the Ministry of Education and National Research Foundation of Korea(2021DG033010102).

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