Journal of Bio-Environment Control (생물환경조절학회지)

The Korean Society for Bio-Environment Control (한국생물환경조절학회)
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Development and Verification of Smart Greenhouse Internal Temperature Prediction Model Using Machine Learning Algorithm

기계학습 알고리즘을 이용한 스마트 온실 내부온도 예측 모델 개발 및 검증

  • Oh, Kwang Cheol (Agriculture and Life Science Research Institute, Kangwon National University) ;
  • Kim, Seok Jun (Department of Interdisciplinary Program in Smart Agriculture, Kangwon National University) ;
  • Park, Sun Yong (Department of Interdisciplinary Program in Smart Agriculture, Kangwon National University) ;
  • Lee, Chung Geon (Agriculture and Life Science Research Institute, Kangwon National University) ;
  • Cho, La Hoon (Department of Interdisciplinary Program in Smart Agriculture, Kangwon National University) ;
  • Jeon, Young Kwang (Department of Interdisciplinary Program in Smart Agriculture, Kangwon National University) ;
  • Kim, Dae Hyun (Department of Biosystems Engineering, Kangwon National University)
  • 오광철 (강원대학교 농업생명과학연구원) ;
  • 김석준 (강원대학교 스마트농업융합학과) ;
  • 박선용 (강원대학교 스마트농업융합학과) ;
  • 이충건 (강원대학교 농업생명과학연구원) ;
  • 조라훈 (강원대학교 스마트농업융합학과) ;
  • 전영광 (강원대학교 스마트농업융합학과) ;
  • 김대현 (강원대학교 바이오시스템기계공학)
  • Received : 2022.03.21
  • Accepted : 2022.06.20
  • Published : 2022.07.31
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Abstract

This study developed simulation model for predicting the greenhouse interior environment using artificial intelligence machine learning techniques. Various methods have been studied to predict the internal environment of the greenhouse system. But the traditional simulation analysis method has a problem of low precision due to extraneous variables. In order to solve this problem, we developed a model for predicting the temperature inside the greenhouse using machine learning. Machine learning models are developed through data collection, characteristic analysis, and learning, and the accuracy of the model varies greatly depending on parameters and learning methods. Therefore, an optimal model derivation method according to data characteristics is required. As a result of the model development, the model accuracy increased as the parameters of the hidden unit increased. Optimal model was derived from the GRU algorithm and hidden unit 6 (r2 = 0.9848 and RMSE = 0.5857℃). Through this study, it was confirmed that it is possible to develop a predictive model for the temperature inside the greenhouse using data outside the greenhouse. In addition, it was confirmed that application and comparative analysis were necessary for various greenhouse data. It is necessary that research for development environmental control system by improving the developed model to the forecasting stage.

본 연구는 데이터를 기반으로 한 인공지능 기계학습 기법을 활용하여 온실 내부온도 예측 시뮬레이션 모델을 개발을 수행하였다. 온실 시스템의 내부온도 예측을 위해서 다양한 방법이 연구됐지만, 가외 변인으로 인하여 기존 시뮬레이션 분석방법은 낮은 정밀도의 문제점을 지니고 있다. 이러한 한계점을 극복하기 위하여 최근 개발되고 있는 데이터 기반의 기계학습을 활용하여 온실 내부온도 예측 모델 개발을 수행하였다. 기계학습모델은 데이터 수집, 특성 분석, 학습을 통하여 개발되며 매개변수와 학습방법에 따라 모델의 정확도가 크게 변화된다. 따라서 데이터 특성에 따른 최적의 모델 도출방법이 필요하다. 모델 개발 결과 숨은층 증가에 따라 모델 정확도가 상승하였으며 최종적으로 GRU 알고리즘과 숨은층6에서 r2 0.9848과 RMSE 0.5857℃로 최적 모델이 도출되었다. 본 연구를 통하여 온실 외부 데이터를 활용하여 온실 내부온도 예측 모델 개발이 가능함을 검증하였으며, 추후 다양한 온실데이터에 적용 및 비교분석이 수행되어야 한다. 이후 한 단계 더 나아가 기계학습모델 예측(predicted) 결과를 예보(forecasting)단계로 개선하기 위해서 데이터 시간 길이(sequence length)에 따른 특성 분석 및 계절별 기후변화와 작물에 따른 사례별로 개발 모델을 관리하는 등의 다양한 추가 연구가 수행되어야 한다.

Keywords

Acknowledgement

이 논문은 2022년도 정부(교육부)의 재원으로 한국연구재단의 지원을 받아 수행된 기초연구사업임(2021R1A6A1A0304424211 and 2022R1C1C2009821).

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