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Vol. 119, Issue 8, August 2010, pp.182-192




Identification of Natural Ventilation Parameters in a Greenhouse with Continuous Roof Vents, Using a PSO and GAs


1Abdelhafid Hasni, 1Belkacem Draoui, Mahieddine LATFAOUI and 2Thierry Boulard

 1Institut de Génie Mécanique, Centre Universitaire de Béchar

B. P. 417, 08000 Béchar, Algérie

Tel.: 213 49 81 55 81/91, fax: 213 49 81 52 44

2INRA-URIH 400, route des Chappes, BP 167, 06903 Sophia Antipolis, France

Tel.: (0)492386663, fax: (0)493653318

E-mail: hasni_haf@yahoo.fr, boulard@sophia.inra.fr



Received: 3 May 2010   /Accepted: 17 August 2010   /Published: 31 August 2010


Abstract: Although natural ventilation plays an important role in the affecting greenhouse climate, as defined by temperature, humidity and CO2 concentration, particularly in Mediterranean countries, little information and data are presently available on full-scale greenhouse ventilation mechanisms. In this paper, we present a new method for selecting the parameters based on a particle swarm optimization (PSO) algorithm and a genetic algorithm (GA) which optimize the choice of parameters by minimizing a cost function. The simulator was based on a published model with some minor modifications as we were interested in the parameter of ventilation. The function is defined by a reduced model that could be used to simulate and predict the greenhouse environment, as well as the tuning methods to compute their parameters. This study focuses on the dynamic behavior of the inside air temperature and humidity during ventilation. Our approach is validated by comparison with some experimental results. Various experimental techniques were used to make full-scale measurements of the air exchange rate in a 400 m2 plastic greenhouse. The model which we propose based on natural ventilation parameters optimized by a particle swarm optimization was compared with the measurements results. Furthermore, the PSO and the GA are used to identify the natural ventilation parameters in a greenhouse. In all cases, identification goal is successfully achieved using the PSO and compared with that obtained using the GA. For the problem at hand, it is found that the PSO outperforms the GA.


Keywords: Natural ventilation, Identification, Genetic algorithm, Particle swarm optimization, Greenhouses, Temperature, Humidity, Hydric model, Climate models, Cooling fog system


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