Article | . 2018 Vol. 36, Issue. 5
Prediction Model of Internal Temperature using Backpropagation Algorithm for Climate Control in Greenhouse



Department of Clean Fuel & Power Generation, Korea Institute of Machinery & Materials1
Protected Horticulture Research Institute, National Institute of Horticultural and Herbal Science2
Faculty of Information Technology, Ton Doc Thang University3
College of Electrical and Computer Engineering, Chungbuk National University4




2018.. 713:729


PDF XML




Greenhouse growers are spending a lot of money on energy management, such as for heating, cooling and CO2 enrichment. To date, many studies have been conducted on energy-consumption prediction models in greenhouses. However, no study has examined ventilation controls for energy saving for a given geographical location. The objective of this study was to use the predicted internal temperature from an Artificial Neural Network (ANN) model to control the ventilation system and to reduce energy costs in greenhouses. For developing the model, we carried out the preprocessing of collected data. First, to detect and eliminate the noise from sensors, we used the Kalman filter algorithm. Then, the dimensions of these data were reduced using Pearson Correlation Coefficient analysis to enhance the accuracy of the model. The ANN model was developed using a backpropagation algorithm, which is a supervised learning method for calculating the weight of nodes. The Levenberg-Marqardt method was used as a learning algorithm. Hyperbolic Tangent was also used as an active function for continuous differentiation of weights of the ANN. This study found that the root mean square errors of the ANN, Multiple Regression Model and Recurrent Neural Network were 1.723, 1.834 and 1.971 respectively. Therefore, the ANN predicted value was more accurate than other prediction models. The predicted greenhouse temperature was used to control greenhouse ventilation. Ventilation of windward and leeward sides was controlled separately by the P-band. The control of ventilation can be performed by different ranges of the P-band for different seasons. Applying predicted temperature data for the P-band’s range based on the ANN to control ventilation can minimize energy loss by opening and closing the window in advance.



1. Bukharov OE, Bogolyubov DP (2015) Development of a decision support system based on neural networks and a genetic algorithm. Exp Syst Appl 42:6177-6183. doi:10.1016/j.eswa.2015.03.018  

2. Burgers G, van Leeuwen PJ, Evensen G (1998) Analysis scheme in the ensemble Kalman filter. Mon Weather Rev 126:1719–1724. doi: 10.1175/1520-0493(1998)126<1719:ASITEK>2.0.CO;2  

3. Christian K, Nobuo Y, Masao F (2004) Levenberg-Marquardt methods with strong local convergence properties for solving nonlinear equations with convex constraints. J Comput Applied Math 172(2):375-397. doi:10.1016/j.cam.2004.02.013  

4. Famili A, Shen WM, Weber R, Simoudis E (1997) Data preprocessing and intelligent data analysis. Intell Data Analy 1:3-23. doi:10.1016/S1088-467X(98)00007-9  

5. Ferreiraa PM, Fariab EA, Ruanoa AE (2002) Neural network models in greenhouse air temperature prediction. Neuro Comput 43:51-75. doi:10.1016/S0925-2312(01)00620-8  

6. Fourati F, Chtourou M (2007) A greenhouse control with feed-forward and recurrent neural networks. Stimul Model Pract Theory 15:1016-1028. doi:10.1016/j.simpat.2007.06.001  

7. He F, Ma C (2010) Modeling greenhouse air humidity by means of artificial neural network and principal component analysis. Comput Electron Agric 71:S19-S23. doi:10.1016/j.compag.2009.07.011  

8. Hill T, Marquez L, O’Connor M, Remus W (1994) Artificial neural network models for forecasting and decision making. Int J Forecast 10:5-15. doi:10.1016/0169-2070(94)90045-0  

9. Hong SW, Lee IB (2014) Predictive model of micro-environment in a naturally ventilated greenhouse for a model based control approach Protected Hortic Plant Fac 23:181-191. doi:10.12791/KSBEC.2014.23.3.181  

10. Kalman RE (1960) A new approach to linear filtering and prediction problems. J Basic Eng. doi:10.1115/1.3662552   

11. Leonard J, Kramer MA (1990) Improvement of the backpropagation algorithm for training neural networks. Comput Chem Eng 14:337-341. doi:10.1016/0098-1354(90)87070-6  

12. Linker R, Seginer I, Gutman PO (1998) Optimal CO control in a greenhouse modeled with neural networks. Comput Electron Agric 19:289-310. doi:10.1016/S0168-1699(98)00008-8  

13. Lourakis MIA (2005) A brief description of the Levenberg-Marquardt algorithm implemented by levmar. Found Res Technol 4:1-6  

14. Ooteghem RJC (2010) Optimal control design for a solar greenhouse. IFAC Proceedings 43:304-309. doi:10.3182/20101206-3-JP- 3009.00054  

15. Patil SL, Tantau HJ, Salokhe VM (2008) Modelling of tropical greenhouse temperature by auto regressive and neural network models. Biosyst Eng 99:423-431. doi:10.1016/j.biosystemseng.2007.11.009  

16. Pino-Mejías R, Pérez-Fargallo A, Rubio-Bellido C, Pulido-Arcas JA (2017) Comparison of linear regression and artificial neural networks models to predict heating and cooling energy demand, energy consumption and CO emissions. Energy 118:24-36. doi:10.1016/ j.energy.2016.12.022  

17. Rural Development Administration (RDA) (2015) Agriculture and livestock income database for improving agricultural management in 2015. RDA, Jeonju, Korea (in Korean)  

18. Seo KK, Kim YS, Park JS (2011) Design of adaptive neuro-fuzzy inference system based automatic control system for integrated environment management of ubiquitous plant factory. J Bio-Environ Control 20:169-175  

19. Trejo-Perea M, Herrera-Ruiz G, Rios-Moreno J, Miranda RC, Rivas-Araiza E (2009) Greenhouse energy consumption prediction using neural networks models. Int J Agric Biol 11:1-6  

20. Wang L, Zeng Y, Chen T (2015) Back propagation neural network with adaptive differential evolution algorithm for time series forecasting. Exp Syst Appl 42:855-863. doi:10.1016/j.eswa.2014.08.018