Journal of The Korean Society of Agricultural Engineers (한국농공학회논문집)

The Korean Society of Agricultural Engineers (한국농공학회)
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Data-Based Model Approach to Predict Internal Air Temperature of Greenhouse

데이터 기반 모델에 의한 온실 내 기온 변화 예측

  • 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)
  • Received : 2015.01.20
  • Accepted : 2015.03.20
  • Published : 2015.05.30
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Abstract

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.

Keywords

References

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