(2023) 기계학습을 활용한 지하 공동구 내부 환경 예측
Predicting Internal Environment of Underground Utility Tunnel Using Machine Learning Model
학회지 : Journal of Korean Institute of Architectural Sustainable Environment and Building Systems
저자 : 김다인, 이인복*, 정우석, 이병진
개요
Underground utility tunnel is located underground, which makes the ventilation system operation and internal environment monitoring important. By designing a machine learning regression model for predicting the internal environment, the effects of the input variables were verified. Temperature, relative humidity and fan operation hour data was collected hourly by sensors installed near the exhaust fan. The external weather data was obtained from the nearest meteorological station in the same experimental period. The results of machine learning regression analysis model showed accuracy of random forest (R2: 0.891), support vector regression (0.800), k-nearest neighbor (0.774), and multi linear regression (0.744). In order to design the accurate model, the n estimator was set to 157, in which the accuracy of temperature prediction was 0.837 by R2, and the accuracy of humidity prediction was 0.950. Using the RF model, the correlating factors correlating were verified and the internal environment of underground utility pipe according to fan operation time was verified.
키워드 : 랜덤 포레스트, 상대습도, 온도, 팬 가동, 회귀분석
링크 : https://doi.org/10.22696/jkiaebs.20230015
Predicting Internal Environment of Underground Utility Tunnel Using Machine Learning Model
학회지 : Journal of Korean Institute of Architectural Sustainable Environment and Building Systems
저자 : 김다인, 이인복*, 정우석, 이병진
개요
Underground utility tunnel is located underground, which makes the ventilation system operation and internal environment monitoring important. By designing a machine learning regression model for predicting the internal environment, the effects of the input variables were verified. Temperature, relative humidity and fan operation hour data was collected hourly by sensors installed near the exhaust fan. The external weather data was obtained from the nearest meteorological station in the same experimental period. The results of machine learning regression analysis model showed accuracy of random forest (R2: 0.891), support vector regression (0.800), k-nearest neighbor (0.774), and multi linear regression (0.744). In order to design the accurate model, the n estimator was set to 157, in which the accuracy of temperature prediction was 0.837 by R2, and the accuracy of humidity prediction was 0.950. Using the RF model, the correlating factors correlating were verified and the internal environment of underground utility pipe according to fan operation time was verified.
키워드 : 랜덤 포레스트, 상대습도, 온도, 팬 가동, 회귀분석
링크 : https://doi.org/10.22696/jkiaebs.20230015