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2022 Vol.31, Issue 3 Preview Page

Original Articles

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

Kwang Cheol Oh1
,
Seok Jun Kim2
,
Sun Yong Park2
,
Chung Geon Lee1
,
La Hoon Cho2
,
Young Kwang Jeon2
,
Dae Hyun Kim3*
오 광철1
,
김 석준2
,
박 선용2
,
이 충건1
,
조 라훈2
,
전 영광2
,
김 대현3*
1Researcher, Agriculture and Life Science Research Institute, Kangwon National University, Chuncheon 24341, Korea
2Graduate Student, Department of Interdisciplinary Program in Smart Agriculture, Kangwon National University, Chuncheon 24341, Korea
3Professor, Department of Biosystems Engineering, Kangwon National University, Chuncheon 24341, Korea
1강원대학교 농업생명과학연구원 연구원
2강원대학교 스마트농업융합학과 대학원생
3강원대학교 바이오시스템기계공학 교수
*Corresponding Author
31 July 2022. pp. 152-162
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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°C). 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.

Keywords
artificial intelligence
machine learning
simulation model
smart greenhouse
time series data

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 숨은층 6에서 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.

키워드
기계학습
시계열 데이터
시뮬레이션 모델
인공지능
자동화 온실
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Information
  • Publisher :The Korean Society for Bio-Environment Control
  • Publisher(Ko) :(사)한국생물환경조절학회
  • Journal Title :Journal of Bio-Environment Control
  • Journal Title(Ko) :생물환경조절학회지
  • Volume : 31
  • No :3
  • Pages :152-162
  • Received Date : 2022-03-21
  • Revised Date : 2022-06-10
  • Accepted Date : 2022-06-20
  • DOI :https://doi.org/10.12791/KSBEC.2022.31.3.152
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