Thermal, ANFIS, and Polynomial Neural Network Models for Predicting Environmental Variables in an Arch Greenhouse

Document Type : Original Research

Authors
J. Javadi Moghaddam 1
D. Momeni 2
G. Zarei 1
1 Agricultural Engineering Research Institute (AERI), Agricultural Research Education and Extension Organization (AREEO), Karaj, Islamic Republic of Iran.
2 Agricultural Engineering Research Institute (AERI), Agricultural Research Education and Extension Organization (AREEO), Karaj, Iran
Abstract
The aim of this study was to design an Adaptive Neuro-Fuzzy Inference Mechanism (ANFIS) and a Polynomial Neural-Network (PNN) to improve modeling and identification of some climate variables within a greenhouse. Furthermore, a Stable Deviation Quantum-Behaved Particle Swarm Optimization (SD-QPSO) algorithm was employed as a learning algorithm to train the constant parameters of ANFIS and PNN structures. To denoise measured data, a wavelet transform method was applied to ensure that no measured data exceeds a predefined interval. Moreover, to show the modeling performance, a set of differential equations were derived as a dynamical model based on the computation of energy and mass balance in a specified greenhouse. The results of modeling and simulation were evaluated with the experimental results of an experimental arch greenhouse. The results showed that the proposed models were more accurate in predicting greenhouse climate and could be used more easily. Moreover, this study showed that the PNN model with less pop-size and evaluation function was more effective than the ANFIS structure to predict the temperatures of inside air and inside roof cover. In this study, an on-line identification system is also proposed for real time identification of experimental data. The obtained simulation results show that performance of the proposed modeling structures and identification system are effective to predict and identify the soil surface, internal air, and roof cover temperatures of the greenhouse. This study shows that the identification algorithm can be used to predict and confirm the results of the model.

Keywords

Subjects

1. Chen, J., Yang, J., Zhao, J., Xu, F., Shen, Z. and Zhang, L. 2016. Energy demand forecasting of the greenhouses using nonlinear models based on model optimized prediction method. Neurocomputing., 174Part B :1087-1100.
2. De-Zwart, H. F. 1996. Analyzing energy-saving options in greenhouse cultivation using a simulation model. PhD Dissertation, Agricultural University. Wageningen, the Netherlands., page: 236.
3. Ferreira, P. M., Faria, E. A. and Ruano, A. E. 2002. Neural network models in greenhouse air temperature Prediction. Neurocomputing., 43:51–75.
4. Fidaros, D. K., Baxevanou, C. A., Bartzanas, T. and Kittas, C. 2010. Numerical simulation of thermal behavior of a ventilated arc greenhouse during a solar day. Renewable Energy., 35:1380–1386.
5. Floquet, T. and Barbot, J. P. 2007. Super twisting algorithm-based step-by-step sliding mode observers for nonlinear systems with unknown inputs. International Journal of Systems Science., 38(10):803–815.
6. Fourati, F. and Chtourou, M. 2007. A greenhouse control with feed-forward and recurrent neural networks. Simulation Modelling Practice and Theory., 15:1016–1028.
7. Fridman, L., Levant, A. and Davila, J. 2007. Observation of linear systems with unknown inputs via high-order sliding-modes. International Journal of Systems Science. 38 (10): 773-791.
8. García, A. E., Zarazúa, G. M. S., Ayala, M. T., Araiza, E. R. and Barrios, A. G. 2020. Applications of Artificial Neural Networks in Greenhouse Technology and Overview for Smart Agriculture Development. Appl. Sci., 10:3835. https://doi.org/10.3390/app10113835.
9. Grigoriu, R. O., Voda, A., Arghira, N. and Iliescu, S. S. 2015. Modelling of Greenhouse using Parabolic Trough Collectors Thermal Energy. IFAC-Papers On Line., 48(30): 450–455.
10. González, I. and Calderón, A. J. 2018. Neural Networks-Based Models for Greenhouse Climate Control. DOI: https://doi.org/10.17979/spudc.9788497497565.0875.
11. He, F. and Ma, C. 2010. Modeling greenhouse air humidity by means of artificial neural network and principal component analysis. Computers and Electronics in Agriculture., 71: 19–23.
12. Hongkang, W. Li, L., Yong, W., Fanjia, M., Haihua, W. and Sigrimis, N. A. 2018. Recurrent Neural Network Model for Prediction of Microclimate in Solar Greenhouse. IFAC PapersOnLine., 51-17:790-795.
13. Hu, H., Xu, L., Goodman, E. D. and Zeng, S. 2014. NSGA-II-based nonlinear PID controller tuning of greenhouse climate for reducing costs and improving performances. Neural Computing and Applications., 24 (3-4): 927-936.
14. Isaev, S. M., and Sadykov, Z. D. 2014. A Mathematical Model of Heat Exchange Control in Solar Greenhouses. ISSN 0003 701X. Applied Solar Energy., 50 (2):103–109.
15. Jang, J. S. R. 1993. ANFIS: adaptive-network-based fuzzy inference system, IEEE Transactions on Systems. Man and Cybernetics., 23(3).
16. Joudi, K. and Farhan, A. 2007. A dynamic model and an experimental study for the internal air and soil temperatures in an innovative greenhouse. Energy Conversion and Management., 91: 76–82.
17. Jung, D. H., Kim, H. S., Jhin, C., Kim, H. J. and Park, S. H. 2020. Time-serial analysis of deep neural network models for prediction of climatic conditions inside a greenhouse. Computers and electronics in Agriculture., 173:105402.
18. Kurtulus, B., Flipo, N. and Goblet, P. 2010. Sensitivity Analysis on an Adaptive Neuro Fuzzy Inference System (ANFIS) for Hydraulic Head Interpolation: Orgeval Experimental SITE/FRANCE, XVIII International Conference on Water Resources CMWR 2010 J. Carrera (Ed), ©CIMNE, Barcelona.
19. Levant, A. 1993. Sliding order and sliding accuracy in sliding mode control. International Journal of Control., 58(6):1247–1263.
20. Márquez-Vera, M. A., Ramos-Fernández, J. C. and Cerecero-Natale, L. F. 2016. Temperature control in a MISO greenhouse by inverting its fuzzy model. Computers and Electronics in Agriculture., 124 :168–174.
21. Miranda, A. C. and Castaño, V. M. 2017. Smart frost control in greenhouses by neural networks models. Computers and Electronics in Agriculture., 137:102-114.
22. Mobtaker, H. G., Ajabshirchi, Y., Ranjbar, S. F. and Matloobi, M. 2016. Solar energy conservation in greenhouse: Thermal analysis and experimental validation. Renewable Energy., 96:509-519.
23. Moghaddam, J. J. and Bagheri, A. 2015. A novel stable deviation quantum-behaved particle swarm optimization to optimal piezoelectric actuator and sensor location for active vibration control. Proceeding of the Institution of Mechanical Engineers, Part I: Journal of System and Control Engineers., 229(6):485-494.
24. Pasgianos, G. D., Arvanitis, K. G., Polycarpou, P. and Sigrimis, N. 2003. A nonlinear feedback technique for greenhouse environmental control. Computers and Electronics in Agriculture., 40:153-177.
25. Patila, S. L., Tantaua, H. J. and Salokheb, V. M. 2008. Modelling of tropical greenhouse temperature by auto regressive and neural network models. Biosystems engineering., 99:423-431.
26. Perez-Gonzalez, A., Begovich-Mendoza, O. and Ruiz-Leon, J. 2017. Modeling of a Greenhouse Prototype Using PSO and Differential Evolution Algorithms Based on a Real-Time LabViewTM Application. Applied Soft Computing., 62:86 – 100.
27. Rosas, A. R., Molina-Aiz, F. D., Valera, D. L., López, A. and Khamkure, S. 2017. Development of a single energy balance model for prediction of temperatures inside a naturally ventilated greenhouse with polypropylene soil mulch. Computers and Electronics in Agriculture., 142:9–28.
28. Sethi, V. P., Sumathy, K., Lee, C. and Pal, D. S. 2013. Thermal modeling aspects of solar greenhouse microclimate control: A review on heating technologies. Solar Energy., 96:56–82.
29. Speetjens, S. L., Stigter, J. D. and Straten, G. V. 2009. Towards an adaptive model for greenhouse control. Computers and Electronics in Agriculture., 67:1–8.
30. Su, Y. and Xu, L. 2017. Towards discrete time model for greenhouse climate control. Engineering in Agriculture, Environment and Food., 10(2):157-170.
31. Su, Y., Xu, L. and Li, D. 2016. Adaptive Fuzzy Control of a Class of MIMO Nonlinear System with Actuator Saturation for Greenhouse Climate Control Problem. IEEE Transactions on Automation Science and Engineering., 13(2):772 - 788.
32. Taki, M., Ajabshirchi, Y., Ranjbar, S. F., Rohani, A. and Matloobi, M. 2016. Heat transfer and MLP Neural Network models to predict inside environment variables and energy lost in a semi-solar greenhouse. Energy and Buildings., 110:314-329.
33. To, A. C., Moore, J. R. and Glaser, S. D. 2009. Wavelet denoising techniques with applications to experimental geophysical data, Wavelet denoising techniques with applications to experimental geophysical data. Signal Processing., 89(2):144-160.
34. Vadiee, A. 2011. Energy Analysis of the Closed Greenhouse Concept-Toward one Sustainable Energy Pathway. KTH School of Industrial Engineering and Management Department of Energy Technology Division of Heat and Power Technology., SE-100 44 Stockholm.
35. Van-Ooteghem, R. J. C. 2007. Optimal Control Design for a Solar Greenhouse, Systems and Control. Wageningen: Wageningen University.
36. Van-Straten, G., Van-Willigenburg, G., Van-Henten, E. and Van-Oothghem, R. 2011. Optimal control of 682 greenhouse cultivation. CRC press, Taylor and Francis, New York.
37. Yau, H. T. and Chen, C. L. 2011. Fuzzy sliding mode controller design for maximum power point tracking control of a solar energy system. Transactions of the Institute of Measurement and Control., 34(5):557–565.
38. Yu, H., Chen, Y., Gul Hassan, S. and Li, D. 2016. Prediction of the temperature in a Chinese solar greenhouse based on LSSVM optimized by improved PSO. Computers and Electronics in Agriculture., 122 : 94–102.
39. Ziapour, B. M. and Dehnavi, R. 2012. A numerical study of the arc-roof and the one-sided roof enclosures based on the entropy generation minimization. Computers and Mathematics with Applications., 64:1636–1648.
Journal of Agricultural Science and Technology
Volume 24, Issue 3
May and June 2022
Pages 617-633