MODELING OF GREENHOUSE CLIMATE USING EVOLUTIONARY ALGORITHMS

Because of Mexican greenhouse industry is growing rapidly, it is important to increase the knowledge on the indoor environmental conditions of greenhouses.
In the current research a first-principles mathematical model to account for the behavior of air temperature, humidity, and soil temperature under greenhouse conditions in the central region of Mexico is studied.
The model structure contains three state variables: air temperature, air humidity, and upper-layer soil temperature.
Also several parameters like wind effect coefficients must be estimated.
However, because of the nonlinear nature of the model, convergence to local minimum was observed as local search methods, such as Nonlinear Least Squares was applied for model calibration.
Therefore, efficient evolutionary algorithms named Differential Evolution (DEAs) algorithms were used.
Convergence of DEAs was more consistent to an apparently global optimum.
Furthermore, a better agreement between predicted and measured values for the three state variables using evolutionary algorithms was observed.
Therefore, Differential Evolution Algorithms can be an alternative to estimate parameters in complex models of greenhouse climate.

International Symposium on High Technology for Greenhouse System Management: Greensys2007

I.L. López-Cruz, A. Ramírez-Arias, A. Rojano-Aguilar, A. Ruiz-García

Greenhouse model, differential evolution algorithms, model calibration, multimodal optimization, mechanistic model

https://doi.org/10.17660/ActaHortic.2008.801.42