Optimization, Modeling and Simulation of Microclimate and Eenergy Management of the Greenhouse by Modeling the Associated Heating and Cooling Systems and Implemented by a Fuzzy Logic Controller using Artificial Intelligence

Faouzi Didi, Nacereddine Bibi-Triki, Bentchikou Mohamed, Abderrahmane Abène


Agricultural greenhouse aims to create a favorable microclimate to the requirements of growth and development of culture, from the surrounding weather conditions, produce according to the cropping calendars fruits, vegetables and flower species out of season and widely available along the year. It is defined by its structural and functional architecture, the quality thermal, mechanical and optical of its wall, with its sealing level and the technical and technological accompanying. The greenhouse is a very confined environment, where multiple components are exchanged between key stakeholders and them factors are light, temperature and relative humidity. This state of thermal evolution is the level sealing of the cover of its physical characteristics to be transparent to solar, absorbent and reflective of infrared radiation emitted by the enclosure where the solar radiation trapping effect otherwise called "greenhouse effect" and its technical and technological means of air that accompany. New climate driving techniques have emerged, including the use of control devices from the classic to the use of artificial intelligence such as neural networks and / or fuzzy logic, etc... As a result, the greenhouse growers prefer these new technologies while optimizing the investment in the field to effectively meet the supply and demand of these fresh products cheaply and widely available throughout the year. In north Africa, greenhouse cultivation is undergoing significant development. To meet an increasingly competitive market and conditioned by increasingly stringent quality standards, "Greenhouse" production systems (heating and air-conditioning systems) Become considerably sophisticated and then disproportionately expensive. That is why locks who want to remain competitive must optimize their investment by controlling production conditions. The aim of our work is to model heating and air conditioning systems whose goal of heating and cooling the air inside our model and implemented in our application of climate control are due to the fuzzy logic that Has the role of optimizing the cost of the energy supplied using MATLAB software.

Full Text:



Didi Faouzi, N. Bibi Triki and A. Chermitti, 2016. Optimizing the greenhouse micro-climate management by the introduction of artificial intelligence using fuzzy logic. Int. J. Computer Eng. Technology, 7: 78-92 , Volume 7, Issue 3, May-June 2016, pp. 78–92, Article ID: IJCET_07_03_007.

Didi Faouzi, N. Bibi-Triki, B. Draoui, A. Abène, 2016 , Modeling, Simulation and Optimization of- agricultural greenhouse microclimate by the application of-artificial intelligence and/or fuzzy logic, International journal of scientific & engineering research, volume 7, issue 8, august-2016 issn 2229-5518.

Didi Faouzi, N. Bibi-Triki, B. Draoui, A. Abène, 2016 Comparison of modeling and simulation results management micro climate of the greenhouse by fuzzy logic between a wetland and arid region, International Journal of Multidisciplinary Research and Modern Education (IJMRME) ISSN (Online): 2454 - 6119, Volume II, Issue II, 2016 .

Didi Faouzi, N. Bibi-Triki, B. Draoui, A. Abène, 2016, Modeling and Simulation of Fuzzy Logic Controller for the purpose of Optimizing the Management Micro Climate of the Agricultural Greenhouse, MAYFEB Journal of Agricultural Science Vol 2 (2016).

Didi Faouzi , N. Bibi-Triki , B. Draoui , A. Abène, 2017, Greenhouse Environmental Control Using Optimized, Modeled and Simulated Fuzzy Logic Controller Technique in MATLAB SIMULINK, Computer Technology and

Application 7 (2016) 273-286, doi: 10.17265/1934-7332/2016.06.002.

Didi Faouzi, N. Bibi-Triki, B. Draoui, A. Abène. Dated 10th March 2017, The Optimal Management of the Micro Climate of the Agricultural Greenhouse through the Modeling of a Fuzzy Logic Controller, International Knowledge Press, Journal of Global Agriculture and Ecology (JOGAE), 7(1): 1-15, 2017, ISSN: 2454-4205, Ref. No. IKP/JOGAE/17/0102.

Jamisson M.Hill, dynamic modeling of tree growth and energy use in a nursery greenhouse using MTLAB and Simulink, Cornell University, 7/31/2006.

Marco Binotto (May 2014), "Greenhouse climate model an aid to estimate the influence of supplemental lighting on greenhouse climate", School of Science and Engineering at Reykjavík University.

Babuska, R., & Mamdani, E. H. (2008). Fuzzy Control. http://www.scholarpedia.org/article/Fuzzy_control.

[10] Breemen, A. v., & Vries, T. d. (2000). An Agent-Based Framework for Designing Multi- Controller Systems. Paper presented at the Proceedings of the Fifth International Conference on The Practical Applications of Intelligent Agents and Multi-Agent Technology, Manchester, U.K.

[11] Tan, V., Yoo, D.-S., & Yi, M.-J. (2008a). A Multiagent-System Framework for Hierarchical Control and Monitoring of Complex Process Control Systems. Paper presented at the Proceedings of the 11th Pacific Rim International Conference on Multi-Agents: Intelligent Agents and Multi-Agent Systems, Hanoi, Vietnam.

[12] Choi, J., Oh, S., & Horowitz, R. (2009). Distributed learning and cooperative control for multi-agent systems. Automatica, 45(12), 2802-2814. doi: 10.1016/j.automatica.2009.09.025.

[13] McArthur, S. D. J., Davidson, E. M., Catterson, V. M., Dimeas, A. L., Hatziargyriou, N. D., Ponci, F., & Funabashi, T. (2007). Multi-Agent Systems for Power Engineering Applications-Part I: Concepts, Approaches, and Technical Challenges. 22, 1743- 1752.doi: 10.1109/tpwrs.2007.908471.

[14] Kelly, I. D., & Keating, D. A. (1998). Faster learning of control parameters through sharing experiences of autonomous mobile robots. International Journal of Systems Science 29(7), 783-793.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.