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데이터 기반 모델에 의한 온실 내 기온 변화 예측

Data-Based Model Approach to Predict Internal Air Temperature of Greenhouse

  • Hong, Se Woon (Division M3-BIORES: Measure, Model & Manage Bioresponses, Department of Biosystems, KU Leuven (Katholieke Universiteit Leuven)) ;
  • Moon, Ae Kyung (IT convergence Technology Research Laboratory, Electronics and Telecommunications Research Institute) ;
  • Li, Song (IT convergence Technology Research Laboratory, Electronics and Telecommunications Research Institute) ;
  • Lee, In Bok (Department of Rural Systems Engineering & Research Institute for Agriculture and Life Sciences, College of Agriculture and Life Sciences, Seoul National University)
  • 투고 : 2015.01.20
  • 심사 : 2015.03.20
  • 발행 : 2015.05.30

초록

Internal air temperature of greenhouse is an important variable that can be influenced by the complex interaction between outside weather and greenhouse inside climate. This paper focuses on a data-based model approach to predict internal air temperature of the greenhouse. External air temperature, solar radiation, wind speed and wind direction were measured next to an experimental greenhouse supported by the Electronics and Telecommunications Research Institute and used as input variables for the model. Internal air temperature was measured at the center of three sections of the greenhouse and used as an output variable. The proposed model consisted of a transfer function including the four input variables and tested the prediction accuracy according to the sampling interval of the input variables, the orders of model polynomials and the time delay variable. As a result, a second-order model was suitable to predict the internal air temperature having the predictable time of 20-30 minutes and average errors of less than ${\pm}1K$. Afterwards mechanistic interpretation was conducted based on the energy balance equation, and it was found that the resulting model was considered physically acceptable and satisfied the physical reality of the heat transfer phenomena in a greenhouse. The proposed data-based model approach is applicable to any input variables and is expected to be useful for predicting complex greenhouse microclimate involving environmental control systems.

키워드

참고문헌

  1. Bakker, J.C., 2006. Model application for energy efficient greenhouses in the Netherlands: greenhouse design, operational control and decision support systems. Acta Horticulturae 718: 191-201.
  2. Boulard, T., and A. Baille, 1995. Modelling of air exchange rate in a greenhouse equipped with continuous roof vents. Journal of Agricultural Engineering Research 61: 37-48. https://doi.org/10.1006/jaer.1995.1028
  3. Boulard, T., P. Feuilloley, and C. Kittas, 1997. Natural ventilation performance of six greenhouse and tunnel types. Journal of Agricultural Engineering Research 67: 249-266. https://doi.org/10.1006/jaer.1997.0167
  4. Coelho, J.P., M. Oliveira, and J.B. Cunha, 2005. Greenhouse air temperature predictive control using the particle swarm optimisation algorithm. Computers and Electronics in Agriculture 49(3): 330-344. https://doi.org/10.1016/j.compag.2005.08.003
  5. Dariouchy, A., E. Aassif, K. Lekouch, L. Bouirden, and G. Maze, 2009. Prediction of the intern parameters tomato greenhouse in a semi-arid area using a time-series model of artificial neural networks. Measurement 42(3): 456-463. https://doi.org/10.1016/j.measurement.2008.08.013
  6. Demrati, H., T. Boulard, H. Fatnassi, A. Bekkaoui, H. Majdoubi, H. Elattir, and L. Bouirden, 2007. Microclimate and transpiration of a greenhouse banana crop. Biosystems Engineering 98(1): 66-78. https://doi.org/10.1016/j.biosystemseng.2007.03.016
  7. Ferreira, P.M., E.A. Faria, and A.E. Ruano, 2002. Neural network models in greenhouse air temperature prediction. Neurocomputing 43: 51-75. https://doi.org/10.1016/S0925-2312(01)00620-8
  8. Hong, S.W., and I.B. Lee, 2014. Predictive model of microenvironment in a naturally ventilated greenhouse for a model based control approach. Protected Horticulture and Plant Factory 23(3): 181-191. https://doi.org/10.12791/KSBEC.2014.23.3.181
  9. Haupt, R.L., and S.E. Haupt, 2004. Practical genetic algorithms, second edition. Wiley-Interscience, New Jersey, USA: 215-219.
  10. Hwang, Y.Y., J.W. Lee, and H.W. Lee, 2013. Estimation of overall heat transfer coefficient for single layer covering in greenhouse. Protected Horticulture and Plant Factory 22(2): 108-115. https://doi.org/10.12791/KSBEC.2013.22.2.108
  11. Kim, K.S., 2011. An optimum light environment design of double-stack bed system by using genetic algorithms. Journal of the Korean Society of Agricultural Engineers 53(6): 93-100. https://doi.org/10.5389/KSAE.2011.53.6.093
  12. Kinderlan, M., 1980. Dynamic modelling of greenhouse of environment. Transactions of the ASAE 23: 1232-1237. https://doi.org/10.13031/2013.34752
  13. Kittas, C., N. Katsoulas, and A. Baille, 2001. Transpiration and energy balance of a greenhouse rose crop in Mediterranean summer conditions. Acta Horticulturae 559: 395-400.
  14. Moon, J.P., S.H. Lee, J.K. Kwon, Y.K. Kang, Y.S. Ryou, and S.J. Lee, 2011. Greenhouse heating technology development by using riverbank filtration water. Journal of the Korean Society of Agricultural Engineers 53(6): 145-152 (in Korean). https://doi.org/10.5389/KSAE.2011.53.6.145
  15. Nam, S.W., Y.S. Kim, I.M. Sung, and G.H. Ko, 2012. Cooling efficiency of low pressure compressed air fogging system in naturally ventilated greenhouses. Journal of the Korean Society of Agricultural Engineers 54(5): 49-55 (in Korean). https://doi.org/10.5389/KSAE.2012.54.5.049
  16. Ntoula, E., N. Katsoulas, C. Kittas, A. Youssef, V. Exdaktylos, and D. Berckmans, 2012. Data based modeling approach for greenhouse air temperature and relative humidity. Acta Horticulturae 952: 67-72
  17. Tavakolpour, A.R., I.Z. Mat Darus, O. Tokhi, and M. Mailah, 2010. Genetic algorithm-based identification of transfer function parameters for a rectangular flexible plate system. Engineering Applications of Artificial Intelligence 23(8): 1388-1397. https://doi.org/10.1016/j.engappai.2010.01.005
  18. Teitel, M., G. Ziskind, O. Liran, V. Dubovsky, and R. Letan, 2008. Effect of wind direction on greenhouse ventilation rate, airflow patterns and temperature distributions. Biosystems Engineering 101: 351-369. https://doi.org/10.1016/j.biosystemseng.2008.09.004
  19. Young, P.C., 1984. Recursive estimation and time series analysis. Springer-Verlag, Berlin.
  20. Youssef, A., J. Dekock, S.E. Ozcan, D. Berckmans, N. Katsoulas, and C. Kittas, 2011. Data-based approach to model the dynamic behaviour of greenhouse temperature. Acta Horticulturae 893: 931-938.