Model-based predictive control of greenhouse climate for reducing energy and water consumption

  • Authors:

  • X. Blasco;M. Martínez;J. M. Herrero;C. Ramos;J. Sanchis

  • Affiliations:

  • Predictive Control and Heuristic Optimization Group, Department of Systems Engineering and Control, Universidad Politécnica de Valencia, Camino de Vera S/N, 46022 Valencia, Spain;Predictive Control and Heuristic Optimization Group, Department of Systems Engineering and Control, Universidad Politécnica de Valencia, Camino de Vera S/N, 46022 Valencia, Spain;Predictive Control and Heuristic Optimization Group, Department of Systems Engineering and Control, Universidad Politécnica de Valencia, Camino de Vera S/N, 46022 Valencia, Spain;Predictive Control and Heuristic Optimization Group, Department of Systems Engineering and Control, Universidad Politécnica de Valencia, Camino de Vera S/N, 46022 Valencia, Spain;Predictive Control and Heuristic Optimization Group, Department of Systems Engineering and Control, Universidad Politécnica de Valencia, Camino de Vera S/N, 46022 Valencia, Spain

  • Venue:
  • Computers and Electronics in Agriculture
  • Year:
  • 2007

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Abstract

This work focuses on development of control algorithms by incorporating energy and water consumption to maintain climatic conditions in greenhouse. Advanced control algorithms can supply solutions to modern exploitations. The new developments usually require accurate models (probably multivariable and non-linear ones) and control methodologies capable of using these models. As an additional requirement it is important for the final application to be easy to use, so advanced control will not mean an increase in complexity of the manipulation of the installation. This article shows an alternative to classical climate control. It is based on two fundamental elements: an accurate non-linear model and a model-based predictive control (MBPC) that incorporate energy and water consumption. Genetic algorithms (GAs) play a key role in these two elements because functions to solve are non-convex and with local minima. First of all GAs supply a way to adjust the non-linear model parameters obtained from first principles, and finally GAs open the possibility of using non-linear model in the MBPC and of establishing a flexible cost index to minimize energy and water consumption. The results on a plastic greenhouse with arch-shaped roofs and for Mediterranean area are presented, important reduction in energy and water used in the cooling system (nebulization) is obtained.