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다원적 회귀 인공 신경망기반 스마트 그린하우스 내부 온·습도 예측에 관한 연구

송영은1, 문애경2, 안수용2, 정회룡3,#orcid

Prediction of Smart Greenhouse Temperature-Humidity Based on Multi-Dimensional LSTMs

Young Eun Song1, Aekyung Moon2, Su-Yong An2, Hoeryong Jung3,#orcid
Journal of the Korean Society for Precision Engineering 2019;36(3):239-246.
Published online: March 1, 2019

1 호서대학교 전기공학과

2 한국전자통신연구원 대경권연구센터

3 건국대학교 기계공학부

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#E-mail: junghl80@konkuk.ac.kr, TEL: +82-2-450-3903
• Received: July 9, 2018   • Revised: October 1, 2018   • Accepted: November 1, 2018

Copyright © The Korean Society for Precision Engineering

This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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  • The objective of this study is to investigate a novel temperature and humidity prediction algorithm for smart greenhouse based on the machine learning method. The smart greenhouse is known to increase farm production by automatically controlling temperature and humidity and other factors. However, maintaining constant inside temperature and humidity in the conventional smart greenhouse system is still a problem because of the multiple time delay elements. To solve the problems, prediction control scheme is required. But, since the system is highly nonlinear with the lack of sensory data, predicting accurate temperature and humidity is very challenging. In this paper, the multi-dimensional Long Short-Term Memory networks (LSTMs) is being applied to deal with the unstructured greenhouse environmental data. The designed LSTMs learning model is trained with the 27 dimensional data which comprises of all the greenhouse control parameter and environmental sensory data. The prediction performance was evaluated using the short, mid and long term experiments. Also, the comparison with the conventional recurrent neural networks (RNNs) based prediction algorithm was done using the experimental results and later on discussions.
  • KEYWORDS: Long short-term memory networks, Recurrent neural networks, Smart greenhouse, Temperature-Humidity Prediction
  • KEYWORDS: 장·단기 메모리 신경망, 회귀 인공 신경망, 스마트 그린하우스, 온·습도 예측

< Controlled input variables of the smart greenhouse >

C1

Curtain 1 (on/off)

C2

Curtain 2 (on/off)

C3

Curtain 3 (on/off)

Cs

Side curtain (on/off)

Thp1

Heat pipe temperature 1 (oC)

Thp2

Heat pipe temperature 2 (oC)

Tv

Ventilation temperature (oC)

Tcv

Ceiling ventilation temperature (oC)

Th

Heater setting temperature (oC)

Wcg

Culture ground water (on/off)

Pc1

Circulating pump 1 (on/off)

Pc2

Circulating pump 2 (on/off)

Hs

Side heater (on/off)

Cel

Ceiling left (on/off)

Cer

Ceiling right (on/off)
< External input variables of the smart greenhouse >

Tdp

Dew point temperature (oC)

Huab

Absolute humidity (g/m3)

WS

Outside wind speed (m/s)

Th

Heater setting temperature (oC)

Wcg

Culture ground water (on/off)

Pc1

Circulating pump 1 (on/off)

Pc2

Circulating pump 2 (on/off)

Hs

Side heater (on/off)

Cel

Ceiling left (on/off)

Cer

Ceiling right (on/off)
< External input variables of the smart greenhouse >

Tdp

Dew point temperature (oC)

Huab

Absolute humidity (g/m3)

WS

Outside wind speed (m/s)

WD

Outside wind direction (degree °)

To

Outside temperature (oC)

R

Rain signal (on/off)

co2

co2 concentration (ppm)

Sr

Solar irradiance (W/m2)

Saf

Accumulated solar flux (J/cm2)

Hug

Inside humidity (%)

Tg

Inside temperature (oC)
예측할 수 없이 계속해서 변화하는 기후와 함께 농업 인구가 지속적으로 감소하고 고령화 되고 있는 사회문제가 더해져 초래되는 식량생산량 감소는 비단 우리나라에서 뿐만 아니라 세계 여러 나라에서 심각한 문제로 자리잡고있다.1 Fig. 1과 같은 스마트 그린하우스 도입이 빠르게 이루어지고 있다.2,3 농작물 생육에 영향을 주는 요소인 히터, 배수기 그리고 차양 막 등의 자동화 장비를 이용하여 지속적으로 제어하고 모니터링 함으로써 관리시간은 줄이고, 농작물의 생산량을 증대 시킬 수 있기 때문이다.4,5 농작물의 생육과 수확량 및 품질에 큰 영향을 미치는 환경요소에는 온도, 습도, 광량 그리고 CO2 농도 등이 있고, 서로 복합적으로 생물학적인 반응을 주고받고 있다.6 특히, 그 중에서도 재배 품종의 최적 온도 및 습도 범위를 유지하지 못하면, 생육에 큰 영향을 끼치고 수확량 저하 등의 직접적인 원인이 될 수 있다. 또한 냉난방비를 비롯한 그린하우스유지비용에도 큰 영향을 줄 수 있다. 따라서 스마트 그린하우스에서는 실시간 온·습도 모니터링이 이슈가 되고 있고, 일정범위 안에서의 정밀한 온·습도 유지 제어가 요구된다.7
Fig. 1

The inside of the smart green house

KSPE_2019_v36n3_239_f001.jpg
하지만 그린하우스 내부 온·습도를 일정하게 유지하는 것은 도전적인 과제이다. 그 이유는 우선 비선형 요소가 많아 정확한 시스템 모델링이 어렵고, 심각한 시간 지연을 일으키는 요소들이 많은 시스템이기 때문이다. 따라서 예를 들면 이러한 시간 지연 효과를 고려하여, 여러가지 환경데이터를 바탕으로 온도가 오르거나 떨어질 것을 미리 예측해, 히터를 포함 여러가지 온도조절장치를 앞서서 작동시켜 온도가 큰 폭으로 떨어지거나 오르는 것을 막을 수 있는 예측 제어가 필요하다. 이를 위해서는 정밀한 그린하우스 내부 온·습도 예측이 선행되어야 한다. 그러나 가용 센서의 종류와 수가 한정적이라 부착 위치에 따른 편차도 상당부분 존재하고, 그 외 영향을 줄 수 있는 비선형 요소가 많기 때문에 정확한 예측이 쉽지 않다.
본 논문에서는 이러한 스마트 그린하우스 내부 온·습도를 정밀하게 예측하기 위해, 여러가지 환경데이터를 이용한 다원 회귀 인공신경망을 기반으로 한 예측 알고리즘을 제안하였다. 다원 회귀 인공신경망은 시퀀스가 긴 데이터에 높은 성능을 보여주는 LSTMs을 이용하였고, 그린하우스 환경 데이터 트레이닝을 위한 LSTMs 구조를 디자인하고 실제 데이터를 이용하여 단기(5분), 중기(30분) 그리고 장기(60분)의 그린하우스 내부 온·습도를 각각 예측하고 성능을 평가하였다.
논문의 구성은 다음과 같다. 2장에서는 관련 최근 연구동향에 대해 정리해 보았다. 실험에 사용된 스마트 그린하우스 시스템은 3장에서 구체화하고, 4장에서는 제안된 회귀 인공 신경망 기반 온·습도 예측 알고리즘에 대해 설명하였다. 5장에서는 온·습도 예측알고리즘 실험 및 성능평가를 결과와 함께 정리하였고, 마지막으로 5장에서는 결론과 함께 스마트 그린하우스의 앞으로 발전 방향 및 새로운 연구주제에 대해 논의하였다.
그린하우스 내부 온도 및 습도는 농작물의 생육을 포함하여 시스템 유지 보수 비용 등에도 직접적인 관련이 있으므로, 이를 보다 정확한 확률로 예측하기 위한 연구가 활발하게 진행되어 왔다. O. Körner8 등은 유동해석과 열해석을 이용한 그린하우스 내부 온도 모델을 기반으로 한 온도 예측알고리즘을 제안하고 시뮬레이션 결과를 보였다. 이러한 모델을 직접 구하는 방법은 비선형요소들이 많은 그린하우스 시스템 특성을 모두 반영하기 어렵고, 시공간에 의존적이라 시스템이 다른 그린하우스에 적용할 경우 성능이 떨어질 수 있는 단점이 있다. J. C. Roy9 등은 Computational Fluid Dynamics (CFD)를 적용하여 온·습도 모델을 구하고, 이를 이용한 예측 방법을 제안했다. 그린하우스 설계 및 센서 부착위치 선정 등에 이점이 있지만, 광량, CO2 농도 및 환기를 통한 외기의 유입 등의 많은 종류의 복합적인 요소를 모두 반영하지 못하고, 다양한 시스템에 유연하게 적용되지 못하는 점이 존재한다. 이러한 점을 극복하기 위해, 머신 러닝 기법을 통한 모델링 기법이 활발하게 진행되었다.
H. Frausto10 등은 Auto Regressive Models (ARM)을 이용하여 모델링을 하였고, J. P. Coelho11 등은 Particle Swarm 알고리즘을 기반으로 얻은 모델을 바탕으로 온도를 예측하는 방법을 제안했다. 또한, H. Yu12 등은 Least Square Support Vector Machine (SVM)을 통해 그린하우스 내부 환경정보를 트레이닝 시킨 후 온도를 예측하는 방식을 적용 후 실험을 통해 정확도와 효율성을 평가하였다. 그리고 트레이닝에 입력되는 파라미터가 많은 동적 시스템인 점을 고려하여, 보다 정확한 결과를 얻기 위해 인공신경망을 적용한 연구가 늘고 있다.13-16 특히, 이러한 인공신경망을 이용한 방법은 지속적으로 온도가 컨트롤 되는 스마트 그린하우스에 적합하다. 스마트 그린하우스의 온도와 습도의 경우, 외부로부터의 환경 영향 뿐 아니라, 내부의 히터와 환기 차광 등에 의해 더욱 복잡하고 다이나믹하게 변화할 수 있기 때문이다. M. Outanoute17 등은 인공신경망을 이러한 제어요소가 존재하는 그린하우스에 적용하여, 온도와 상대습도의 다이나믹 모델을 구하고 예측하는 알고리즘을 제안하였다. 또한 데이터가 시간의 흐름에 따라 변화하는 특성에 착안하여, 회귀 인공 신경망을 적용하는 연구 사례가 증가하고 있다.18-20 하지만 종래의 회귀 인공 신경망을 이용할 경우, 모델링 정확도를 높이기 위해 긴 시퀸스 데이터를 이용하여 인공신경망을 학습시키는데, 학습 데이터의 시퀀스가 길어 짐에 따라 역전파 거리가 증가하게 되고, 따라서 앞선 시퀀스 값들과 뒷부분 시퀀스 값들 사이의 연관성이 점차 사라져버린다. 이를 베니싱 문제라 하는데 회귀 인공신경망 예측 결과의 정확도를 떨어트릴 수 있다.21 따라서 이를 극복하기 위해, LSTMs 등의 기법을 이용한 모델링 및 예측방법이 요구된다.
본 논문에서는 토마토 최적 생육을 위해 개발된 스마트 그린하우스 시스템을 사용하였다. 특히, 토마토의 경우, 생장과 과실의 양과 질을 위해 일정 온도와 유지가 필수이다. 따라서 개발된 시스템은 온도를 제어하기 위해, Fig. 2에 나타난 바와 같은 커튼과 환기창 환풍기 등이 설치 되어있고, 그 외에 히팅 시스템과 수분 공급기 등이 설치되어 있다. 스마트 그린하우스의 내부 온도는 이러한 제어입력 변수와 제어할 수 없는 외부입력 변수가 작용하여 내부 온·습도가 결정된다. Fig. 3은 이러한 스마트 그린하우스의 입력/출력 다이어그램을 보여준다.
Fig. 2

The smart greenhouse system for tomato cultivation

KSPE_2019_v36n3_239_f002.jpg
Fig. 3

Input/output diagram of the greenhouse

KSPE_2019_v36n3_239_f003.jpg
커튼은 열고 닫는 On/Off 입력으로 C1, C2, C3, Cs, Cel 그리고 Cer이 있다. 배양지 수분공급 Wcg과 순환펌프 Pc1, Pc2 역시 On/Off 입력이다. Thp1, Thp2는 히트 파이프의 온도 값이다. Tv, Tcv은 환기설정 온도이고, Th는 난방 설정 온도 등으로 총 15개의 제어 입력 변수가 스마트 그린하우스 시스템에 사용된다. 그리고 외부 입력 변수는 Table 1에 나타난 바와 같이 총 11개로 정의할 수 있다. Tdp는 이슬점 온도를 나타내고, Huab, Hug는 각각 내부 절대습도와 상대습도를 나타낸다. 외부 풍속과 풍량은 WS, WD이고, 외부온도는 To이다. 감우 센서로부터 감지되는 값은 R로 On/Off 입력이다. CO2는 이산화탄소 농도이고, Sr, Saf는 각각 광량과 누적 일조량이다. Tg는 스마트 그린하우스 내부 온도이다. 개발된 스마트 그린하우스에서는 이러한 제어입력과 외부입력 변수와 1분 간격으로 내부 무선 센서 노드들과 외부 기상대에 의해 측정되고 기록된다.
Table 1

Training weather data set with controlled input variables

KSPE_2019_v36n3_239_t001.jpg
스마트 그린하우스 내부 온·습도 예측을 위해서 사용되는, 회귀 인공 신경망의 트레이닝에 사용할 입력 값은 앞서 살펴본 제어입력 변수 15개와 외부입력 변수 11개 그리고 시간 t (min)로 총 27개로 구성된다. 내부 온도와 습도는 광량 등과 같은 외부요인으로 인하여 하루 동안 비슷한 추이를 보이는 특징이 있다. 그리고 외부요인에 의한 영향을 충분히 반영하기 위해, 7일간의 데이터를 트레이닝에 사용하였다. 즉, 1분 간격으로 기록된 데이터를 7일간 총 10,080개의 데이터 셋으로 트레이닝한다. 계절에 무관하게 준수한 예측 성능을 유지하기 위하여, 본 논문에서 제안하는 온·습도 예측 방법은 기준 시점에서 이전 일주일간 측정된 온·습도 데이터를 이용하여 인공신경망을 학습시키고 학습된 인공신경망을 이용하여 이후 일주일간의 온·습도를 예측한다. 예를 들어, 1월 첫째 주의 측정 데이터를 이용하여 학습된 인공신경망을 이용하여 1월 둘째 주의 온·습도를 예측하고, 1월 둘째 주의 측정 데이터를 이용하여 학습된 인공신경망을 이용하여 1월 셋째 주의 온·습도를 예측하는 방법이다. 따라서 결과적으로 1 – 3월까지의 데이터가 일주일간격으로 순차적으로 모두 사용되게 된다. 예측 시점과 근접한 환경정보로 학습된 인공신경망을 이용함으로써 온·습도 예측의 정확성을 향상시킬 수 있다.
그리고 본 시스템과 같이 트레이닝 데이터의 시퀀스가 긴 경우, 기존의 RNNs을 이용하여 트레이닝하면 베니싱 문제가 발생하여 모델링 정확도가 감소할 수 있다. 따라서 예측 값 또한 정확도가 감소하게 된다. 따라서 본 논문에서는 LSTMs를 적용하였고, RNNs과 성능 비교를 해보았다. 그 결과는 다음 장에서 실험 결과와 함께 나타내었다. Fig. 4는 LSTMs 구조를 보여준다. Xi는 입력값으로 Xi= [C1i, C2i, C3i, Csi, Thp1i, Thp2i, Tvi, Tcvi, Thi, Wcgi, Pc1i, Pc2i, Hsi, Celi, Ceri, Tdpi, Huabi, WSi, WDi, Toi, Ri, co2i, Sri, Safi, Hugi, Tgi]T이고, Yi는 출력값으로 Yi= [Hugi, Tgi] T이다. Yi의 경우, i번째 시퀀스인 i분 뒤의 데이터 예측 값이다. 따라서 예를 들어, Y5의 경우는 5분, Y30의 경우, 30분 그리고 Y60는 한 시간 뒤의 예측 값이 된다. 본 논문에서는 이를 단기, 중기 그리고 장기로 각각 나누어 내부 온도와 습도의 예측 성능을 실험하였다. 그 이유는 각기 다른 시간 지연을 가진 요소를 가진 시스템을 고려하여, 시간 지연효과가 큰 장비는 장기 예측을 참고하여 제어에 사용하고, 지연효과가 작은 장비는 단기 예측을 각각 이용하게 하여 보다 정밀한 온·습도 제어가 가능케하기 위함이다.
Fig. 4

The LSTMs structure for the smart greenhouse system.

KSPE_2019_v36n3_239_f004.jpg
스마트 그린하우스에는 내부의 온·습도를 제어하기 위한 환기시스템, 수분공급장치, 히터와 같은 제어장비들을 갖추고 있는데, 장비 가동에 따른 온실내부 온·습도의 응답성이 느리기 때문에 제어성능을 높이기 위해서 미리 온습도의 변화를 예측하고 장비를 가동하는 것이 중요하다. 특히, 배지수분공급 장치 및 메인 및 사이드 히터 등은 가동 이후 예열과 같은 과정이 필요해 즉각적으로 온·습도를 제어할 수 없어 사전에 온·습도 변화를 예측하고 장비를 미리 가동시키는 것이 필요하다. 온도변화를 시간의 단계별로 예측할 수 있으면, 각각의 장비 특성에 맞게 운용이 가능하여 온·습도를 보다 일정하게 제어할 수 있어 농작물 생육과 수확량을 더욱 증대할 수 있고, 유지비용 또한 절감할 수 있다. 따라서 본 논문에서 제안하는 방법의 온·습도 예측성능을 단기(5분), 중기(10분), 장기(60분)로 나누어 지연시간에 따른 예측성능을 평가하였다. 그리고 Optimizer는 Adam Optimizer를 사용하였고, Hidden Dimension은 10개를 적용하였다. 또한 마지막에 한 층의 Fully Connected Layer를 거치도록 구성하였다. Max Iteration은 800회로 하였고, Learning Rates는 0.01로 설정하였다.
Table 1과 같이, 실험에 사용한 데이터는 2018년 1월부터 3월까지 스마트 그린하우스 시스템에 기록된 센서 및 제어입력 값이다. 7일간의 데이터를 이용하여 트레이닝한 후 그 다음날부터 5일간의 온·습도를 단기(5분), 중기(30분) 그리고 장기(60분)로 나누어 예측 성능 시험을 구성하였다. 1월부터 3월까지 예측 결과의 평균 제곱근 오차(Root Mean Square Error; RMSE)를 측정하였고, 인공신경망의 Loss 감소 그래프를 구해보았다. 예측성능은 LSTMs와 RNNs 기반의 예측 모델을 각각 비교해 보았다.
5.1 온도 예측 성능 평가
1월, 2월 그리고 3월의 그린하우스 내부 온도 단기, 중기, 장기 예측 RMSE를 비교한 그래프는 Table 2과 같다. 1월의 온도 예측인경신경망 모델의 Loss 감소 그래프는 Fig. 5에 나타나 있고, 해당 예측 모델을 이용한 결과는 Fig. 6에 나타나 있다. 전반적으로 LSTMs를 예측 모델이 RNNs를 이용한 모델보다 Loss 감소도 더 빠르게 일어나고 그 결과 더 정확한 모델링이 가능하여, 결과적으로 온도 예측 또한 더 좋은 성능을 보여준다.
Table 2

RMSE comparison of the smart greenhouse temperature prediction system

Table 1
Month term (min.) RNNs LSTMs
January 5 0.02141 0.01884
30 0.05138 0.05465
60 0.08562 0.08684
February 5 0.02211 0.01912
30 0.05029 0.04114
60 0.08732 0.08631
March 5 0.02032 0.01771
30 0.05054 0.04845
60 0.08912 0.08684
Fig. 5

The temperature prediction model loss graph

KSPE_2019_v36n3_239_f005.jpg
Fig. 6

The temperature prediction results: (a) short term (5min.) prediction result, (b) mid term (30min.) prediction result and long term, (c) long term (60min.) prediction result

KSPE_2019_v36n3_239_f006.jpg
예측 온도 평균오차의 절대값은 단기가 0.273oC, 중기 0.737oC, 그리고 장기일 때 1.111oC 발생했고, 최대오차의 절대값은 단기 1.366oC, 중기 3.752oC 그리고 장기일 때 5.777oC 발생하였다(Fig. 7 참조). RMSE 값과 함께 고려해볼 때 단기에서 장기로 갈수록 예측 오차가 증가함이 확인되었지만, 평균오차가 1.5oC를 벗어나지 않음을 확인할 수 있었다.
Fig. 7

The temperature prediction error results (absolute value)

KSPE_2019_v36n3_239_f007.jpg
5.2 습도 예측 성능 평가
온도 예측과 마찬가지로 RMSE값이 LSTMs가 종래의 RNNS 기반의 예측 모델보다 더 작은 값을 기록했고(Table 3 참조), Loss 또한 더 빠르게 수렴함을 알 수 있다(Fig. 8 참조). Fig. 9은 1월의 데이터를 사용하여 예측한 습도 예측을 보여준다. 단기의 경우 평균오차의 절대치값은1.755%였고, 최대오차의 절대값은 52.701% 발생하였다. 중기에는 평균오차와 최대오차 절대값이 각각 3.115%와 46.35%로 나타났고, 장기 예측 결과는 각각 3.255%, 52.082%로 나왔다(Fig. 10 참조). 2월과 3월의 결과도 전반적으로 유사한 형태로 얻을 수 있었고, 예측 시간이 길어지면 오차도 커지는 경향이 있지만, 평균오차 절대값이 모두 3.42% 범위안에 있었다.
Table 3

RMSE comparison of the smart greenhouse humidity prediction system

Table 3
Month term (min.) RNNs LSTMs
January 5 0.06635 0.06557
30 0.09743 0.08755
60 0.08851 0.08172
February 5 0.06598 0.05356
30 0.07812 0.07073
60 0.08943 0.08163
March 5 0.05998 0.05324
30 0.07198 0.06954
60 0.08461 0.08196
Fig. 8

The humidity prediction model loss graph

KSPE_2019_v36n3_239_f008.jpg
Fig. 9

The humidity prediction results: (a) short term (5 min.) prediction result, (b) mid term (30 min.) prediction result and long term, (c) long term (60 min.) prediction result

KSPE_2019_v36n3_239_f009.jpg
Fig. 10

The humidity prediction error results (absolute value)

KSPE_2019_v36n3_239_f010.jpg
다원적 회귀 인공신경망을 구성하여, 스마트 온실 내부 온도와 습도 예측 실험을 해본결과, LSTMs을 기반으로 한 예측 모델이 기존 RNNs의 베니싱 문제가 나타나지 않아 성능면에서 더 나은 결과를 보였다. 예측해야 할 시간이 길어질 수록 오차가 늘어나는 경향을 띄기는 하였지만, 일정범위를 벗어나지 않으므로 시스템에서 신뢰할 수 있는 데이터로 사용 가능하다. 이러한 결과는, 하루 동안의 온도 변화 추이가 비교적 비슷하기 때문에 예측 결과도 이러한 것을 반영하기 때문으로 해석된다. 1월의 습도 예측의 경우, 500분 근처에서 습도센서 오류가 일어나 잘못된 값이 입력이 되었고, 그 결과 예측 값과의 큰 차이를 보였다. 이를 바탕으로 미루어 볼 때, 온·습도 예측 값을 활용하면 스마트 그린하우스 시스템 제어뿐만 아니라, 시스템 오류 검출에도 활용될 수 있음을 확인할 수 있었다.
하지만, 본 논문에서 사용하는 방법과 같이 트레이닝 데이터와 테스트 데이터가 비슷한 분포를 보일 경우 Overfitting의 가능성이 있어, 향후 연구에서 이를 억제하기 위해 Regularization 기법을 적용하고 평가할 예정이다. 또한, 예측성능을 더욱 향상시키고, 보다 효율적인 시스템 운용을 위해 1년간의 데이터를 이용한 트레이닝과의 예측 성능 비교를 통해, 인공신경망의 업데이트 횟수를 감소시키는 연구를 진행할 예정이다.
향후 스마트 그린하우스 온도 습도제어에 이러한 예측 값들을 활용하면, 시간 지연요소가 있는 구성요소들의 예측제어를 통해 더욱 정밀한 온·습도 유지가 가능하고 전력효율 상승 등의 효과를 얻을 수 있을 것으로 기대된다. 또한 오차 발생요인으로 센서의 위치가 있을 수 있다. 보다 정밀한 데이터 획득을 위한, 최적 센서 위치선정 방법과의 병행 연구가 앞으로 필요하다. 마지막으로 농작물의 생육에 영향을 크게 줄 수 있는 병충해 발생 등과 환경데이터와의 상관관계 분석 또한 요구된다.
이 논문은 2018년도 정부(과학기술정보통신부)의 재원으로 한국연구재단의 지원을 받아 수행된 기초 연구 사업임(No. 2017R1C1B5018218, 2017R1C1B5018171).
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Young Eun Song
KSPE_2019_v36n3_239_bf001.jpg
Assistant Professor in the Department of Electrical Engineering, Hoseo University. His research interest is robotics, artificial intelligence and virtual/augmented reality.
Aekyung Moon
KSPE_2019_v36n3_239_bf002.jpg
Principal researcher in Electronics and Telecommunications Research Institute (ETRI). Her research interest is machine learning, big data analysis and automatic control of smart farm system.
Su-Yong An
KSPE_2019_v36n3_239_bf003.jpg
Senior researcher in Electronics and Telecommunications Research Institute (ETRI). His research interest is mobile robotics, computer vision, and automatic control of smart farm system.
Hoeryong Jung
KSPE_2019_v36n3_239_bf004.jpg
Assistant Professor in the Department of Mechanical Engineering, Konkuk University. His research interest is virtual reality and medical robotics.

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Prediction of Smart Greenhouse Temperature-Humidity Based on Multi-Dimensional LSTMs
J. Korean Soc. Precis. Eng.. 2019;36(3):239-246.   Published online March 1, 2019
DOI: https://doi.org/10.7736/KSPE.2019.36.3.239
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Prediction of Smart Greenhouse Temperature-Humidity Based on Multi-Dimensional LSTMs
J. Korean Soc. Precis. Eng.. 2019;36(3):239-246.   Published online March 1, 2019
DOI: https://doi.org/10.7736/KSPE.2019.36.3.239
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Prediction of Smart Greenhouse Temperature-Humidity Based on Multi-Dimensional LSTMs
Image Image Image Image Image Image Image Image Image Image
Fig. 1 The inside of the smart green house
Fig. 2 The smart greenhouse system for tomato cultivation
Fig. 3 Input/output diagram of the greenhouse
Fig. 4 The LSTMs structure for the smart greenhouse system.
Fig. 5 The temperature prediction model loss graph
Fig. 6 The temperature prediction results: (a) short term (5min.) prediction result, (b) mid term (30min.) prediction result and long term, (c) long term (60min.) prediction result
Fig. 7 The temperature prediction error results (absolute value)
Fig. 8 The humidity prediction model loss graph
Fig. 9 The humidity prediction results: (a) short term (5 min.) prediction result, (b) mid term (30 min.) prediction result and long term, (c) long term (60 min.) prediction result
Fig. 10 The humidity prediction error results (absolute value)

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Fig. 8

Fig. 9

Fig. 10

Prediction of Smart Greenhouse Temperature-Humidity Based on Multi-Dimensional LSTMs
Month term (min.) RNNs LSTMs
January 5 0.02141 0.01884
30 0.05138 0.05465
60 0.08562 0.08684
February 5 0.02211 0.01912
30 0.05029 0.04114
60 0.08732 0.08631
March 5 0.02032 0.01771
30 0.05054 0.04845
60 0.08912 0.08684
Month term (min.) RNNs LSTMs
January 5 0.06635 0.06557
30 0.09743 0.08755
60 0.08851 0.08172
February 5 0.06598 0.05356
30 0.07812 0.07073
60 0.08943 0.08163
March 5 0.05998 0.05324
30 0.07198 0.06954
60 0.08461 0.08196
Table 1 Training weather data set with controlled input variables
Table 2 RMSE comparison of the smart greenhouse temperature prediction system
Table 3 RMSE comparison of the smart greenhouse humidity prediction system

Table 1

Table 2

Table 3

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