GA Optimization of the Coupled Climate Model of an Order Two of a Greenhouse

Lammari, Khelifa; Bounaama, Fateh; Draoui, Belkacem; Mrah, Benyoucef; Haidas, Mohamed

doi:10.1016/j.egypro.2012.05.053

Cited by 10 publications

(5 citation statements)

References 5 publications

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“…The accuracy of the models reported in the bibliography varies considerably. Thus, the model InverSim validated for a 12-day period with a thermal amplitude of T i =3-52ºC (Bouzo et al, 2006) obtained lower accuracy (RMSE=3.9 °C; R 2 =0.88) than models validated with lower thermal amplitudes and time intervals, such as the models used by Lammari et al (2012) and Hasni et al (2011) with climatic data for a period of a week in France, obtaining RMSE=1.05 °C (T i =14-27 ºC) and mean absolute error MAE=1.24 ºC (T i =15-32 ºC). Other models, such as MICGREEN (Singh et al, 2006), were validated using only one day of measurements (T i =14-28 ºC), obtaining values of RMSE=5.69 ºC for inside air, RMSE=3.21 ºC for soil surface temperature, RMSE=3.91 ºC for cover temperature and RMSE=3.70 ºC for canopy temperature.…”

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confidence: 89%

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Development of a single energy balance model for prediction of temperatures inside a naturally ventilated greenhouse with polypropylene soil mulch

Reyes-Rosas

Molina-Aiz

Valera

et al. 2017

Computers and Electronics in Agriculture

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confidence: 89%

“…Hasni et al (2011) used a digital simulation based genetic algorithm (GA) and a particle swarm optimization (PSO) procedure to improve the physical sizes of the model of Boulard et al (1996). Lammari et al (2012) also employed the GA technique to optimize a nonlinear model of an environmental greenhouse.…”

Section: Empirical Modelsmentioning

confidence: 99%

Development of a single energy balance model for prediction of temperatures inside a naturally ventilated greenhouse with polypropylene soil mulch

Reyes-Rosas

Molina-Aiz

Valera

et al. 2017

Computers and Electronics in Agriculture

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“…Lammari et al (2012) use a GA to determine the parameters of a greenhouse model with the aim of optimise both the temperature and vapour pressure. Simulation results were based on data collected in a greenhouse located in France for a tomato crop culture.…”

Section: Review Of Nabi Metaheuristics For Greenhouse Controlmentioning

confidence: 99%

Review of nature and biologically inspired metaheuristics for greenhouse environment control

Oliveira

Pires

Boaventura‐Cunha

et al. 2020

Transactions of the Institute of Measurement and Control

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A significant number of search and optimisation techniques whose principles seek inspiration from nature and biology phenomena have been proposed in the last decades. These methods have been successfully applied to solve a wide range of engineering problems. This is also the case of greenhouse environment control, which has been incorporating this type of techniques into its design. This paper addresses evolutionary and bio-inspired methods in the context of greenhouse environment control. Algorithm principles for reference techniques are reviewed, namely: simulated annealing, genetic algorithm, differential evolution and particle swarm optimisation. The last three techniques are considered using single and multiple objective formulations. A review of these algorithms within greenhouse environment control applications is presented, considering single and multiple objective problems, as well as their current trends.

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“…The GA parameters for the optimization process are exposed in Table 1: Comparisons between actual measurements inside the greenhouse and the MIMO model are exposed in the Figure 4, 5, 6 and Figure 7. The final optimum parameters system settings are enumerated in the (Table 2) [21].…”

Section: Objective Function Of Gamentioning

confidence: 99%

Constrained Ga Pi Sliding Mode Control of Indoor Climate Coupled Mimo Greenhouse Model

Lammari

Bounaama

Ouradj

et al. 2020

Journal of Thermal Engineering

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High accuracy trajectory tracking with constrained inputs is challenging topic in greenhouse indoor environment control. This is due to nonlinearities and inputs/outputs coupling present in physical model of greenhouse. The objective of this study is about the problem of identification and control of a Multi-Input Multi-Output (MIMO) greenhouse process. Proportional Integral (PI) and sliding mode controllers (SMC) are used in conjunction so that estimated outputs tracks desired trajectories with good performance as near as possible in spite of coupled dynamics and outside disturbances climate. To reflect the practical aspect, the constraints of the process and control elements were taken into account .The results demonstrate the feasibility of PISMC control method. Simulation has been done in the environment of Matlab and Simulink; it shows that combined controllers are capable to manage successfully the microclimate of greenhouse.

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GA Optimization of the Coupled Climate Model of an Order Two of a Greenhouse

Cited by 10 publications

References 5 publications

Development of a single energy balance model for prediction of temperatures inside a naturally ventilated greenhouse with polypropylene soil mulch

Development of a single energy balance model for prediction of temperatures inside a naturally ventilated greenhouse with polypropylene soil mulch

Review of nature and biologically inspired metaheuristics for greenhouse environment control

Constrained Ga Pi Sliding Mode Control of Indoor Climate Coupled Mimo Greenhouse Model

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