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Digital Contents Society (한국디지털콘텐츠학회)
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Development of Prediction Model for Greenhouse Control based on Machine Learning

머신러닝 기반의 온실 제어를 위한 예측모델 개발

  • Kim, Sang Yeob (Department of Clean Fuel & Power Generation, Korea Institute of Machinery & Materials) ;
  • Park, Kyoung Sub (Protected Horticulture Research Institute, National Institute of Horticultural and Herbal Science) ;
  • Lee, Sang Min (Department of Clean Fuel & Power Generation, Korea Institute of Machinery & Materials) ;
  • Heo, Byeong Mun (Database/Bioinformatics Lab, School of Electrical & Computer Engineering, Chungbuk National University) ;
  • Ryu, Keun Ho (Database/Bioinformatics Lab, School of Electrical & Computer Engineering, Chungbuk National University)
  • 김상엽 (한국기계연구원 청정연료발전연구실) ;
  • 박경섭 (국립원예특작과학원 시설원예연구소) ;
  • 이상민 (한국기계연구원 청정연료발전연구실) ;
  • 허병문 (충북대학교 데이터베이스/바이오인포매틱스 연구실) ;
  • 류근호 (충북대학교 데이터베이스/바이오인포매틱스 연구실)
  • Received : 2018.04.10
  • Accepted : 2018.04.25
  • Published : 2018.04.30
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Abstract

In this study, we developed a prediction model for greenhouse control using machine learning technique. The prediction model was developed using measured data (2016) on greenhouse in the Protected Horticulture Research Institute. In order to improve the predictive performance of model and to ensure the reliability of data, the dimension of the data was reduced by correlation analysis. The dataset were divided into spring, summer, autumn, and winter considering the seasonal characteristics. An artificial neural network, recurrent neural network, and multiple regression model were constructed as a machine leaning based prediction model and evaluated by comparative analysis with real dataset. As a result, ANN showed good performance in selected dataset, while MRM showed good performance in full dataset.

본 연구는 머신러닝 기법을 이용한 온실 제어를 위한 예측모델을 개발하는 것이 목적이다. 시설원예연구소의 실험온실에서 측정된 데이터(2016년)를 사용하여 예측모델을 개발하였다. 모델의 예측성능 향상과 데이터의 신뢰성 확보를 위해 상관관계분석을 통해 데이터의 축소를 수행하였다. 데이터는 계절별 특성을 고려하여 봄, 여름, 가을 및 겨울로 나누어 구축하였다. 머신러닝 기반의 예측모델로 인공신경망, 순환신경망 및 다중회귀모델을 구축하고 비교분석을 통해 타당성을 평가하였다. 분석 결과에서, Selected dataset에서는 인공신경망 모델이 Full dataset에서는 다중회귀모델이 좋은 예측성능을 보였다.

Keywords

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