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Model Predictive Control of Smart Greenhouses as the Path towards Near Zero Energy Consumption

Author

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  • Chiara Bersani

    (DIBRIS—Department on Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, 16145 Genova, Italy)

  • Ahmed Ouammi

    (Centre for Sustainable Development, Qatar University, Doha 2713, Qatar)

  • Roberto Sacile

    (DIBRIS—Department on Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, 16145 Genova, Italy)

  • Enrico Zero

    (DIBRIS—Department on Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, 16145 Genova, Italy)

Abstract

Modern agriculture represents an economic sector that can mainly benefit from technology innovation according to the principles suggested by Industry 4.0 for smart farming systems. Greenhouse industry is significantly becoming more and more technological and automatized to improve the quality and efficiency of crop production. Smart greenhouses are equipped with forefront IoT- and ICT-based monitoring and control systems. New remote sensors, devices, networking communication, and control strategies can make available real-time information about crop health, soil, temperature, humidity, and other indoor parameters. Energy efficiency plays a key role in this context, as a fundamental path towards sustainability of the production. This paper is a review of the precision and sustainable agriculture approaches focusing on the current advance technological solution to monitor, track, and control greenhouse systems to enhance production in a more sustainable way. Thus, we compared and analyzed traditional versus model predictive control methods with the aim to enhance indoor microclimate condition management under an energy-saving approach. We also reviewed applications of sustainable approaches to reach nearly zero energy consumption, while achieving nearly zero water and pesticide use.

Suggested Citation

  • Chiara Bersani & Ahmed Ouammi & Roberto Sacile & Enrico Zero, 2020. "Model Predictive Control of Smart Greenhouses as the Path towards Near Zero Energy Consumption," Energies, MDPI, vol. 13(14), pages 1-17, July.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:14:p:3647-:d:384787
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    References listed on IDEAS

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    Cited by:

    1. Theodora Karanisa & Alexandre Amato & Renee Richer & Sara Abdul Majid & Cynthia Skelhorn & Sami Sayadi, 2021. "Agricultural Production in Qatar’s Hot Arid Climate," Sustainability, MDPI, vol. 13(7), pages 1-25, April.
    2. Chiara Bersani & Marco Fossa & Antonella Priarone & Roberto Sacile & Enrico Zero, 2021. "Model Predictive Control versus Traditional Relay Control in a High Energy Efficiency Greenhouse," Energies, MDPI, vol. 14(11), pages 1-21, June.
    3. Lin, Dong & Zhang, Lijun & Xia, Xiaohua, 2021. "Model predictive control of a Venlo-type greenhouse system considering electrical energy, water and carbon dioxide consumption," Applied Energy, Elsevier, vol. 298(C).
    4. Chrysanthos Maraveas & Christos-Spyridon Karavas & Dimitrios Loukatos & Thomas Bartzanas & Konstantinos G. Arvanitis & Eleni Symeonaki, 2023. "Agricultural Greenhouses: Resource Management Technologies and Perspectives for Zero Greenhouse Gas Emissions," Agriculture, MDPI, vol. 13(7), pages 1-46, July.
    5. Theodora Karanisa & Yasmine Achour & Ahmed Ouammi & Sami Sayadi, 2022. "Smart greenhouses as the path towards precision agriculture in the food-energy and water nexus: case study of Qatar," Environment Systems and Decisions, Springer, vol. 42(4), pages 521-546, December.
    6. Aaron Chimbelya Siyunda & Emmanuel Chikalipa & Tibonge Mfune & Rodrick Habvumba, 2022. "Digitalizing Agriculture for Sustainable Crop production," International Journal of Science and Business, IJSAB International, vol. 11(1), pages 55-61.
    7. Larisa Hrustek, 2020. "Sustainability Driven by Agriculture through Digital Transformation," Sustainability, MDPI, vol. 12(20), pages 1-17, October.
    8. Sławomir Francik & Bogusława Łapczyńska-Kordon & Norbert Pedryc & Wojciech Szewczyk & Renata Francik & Zbigniew Ślipek, 2022. "The Use of Artificial Neural Networks for Determining Values of Selected Strength Parameters of Miscanthus × Giganteus," Sustainability, MDPI, vol. 14(5), pages 1-26, March.
    9. Chen, Wei-Han & Mattson, Neil S. & You, Fengqi, 2022. "Intelligent control and energy optimization in controlled environment agriculture via nonlinear model predictive control of semi-closed greenhouse," Applied Energy, Elsevier, vol. 320(C).
    10. Chiara Bersani & Carmelina Ruggiero & Roberto Sacile & Abdellatif Soussi & Enrico Zero, 2022. "Internet of Things Approaches for Monitoring and Control of Smart Greenhouses in Industry 4.0," Energies, MDPI, vol. 15(10), pages 1-30, May.
    11. Mahmood, Farhat & Govindan, Rajesh & Bermak, Amine & Yang, David & Al-Ansari, Tareq, 2023. "Data-driven robust model predictive control for greenhouse temperature control and energy utilisation assessment," Applied Energy, Elsevier, vol. 343(C).
    12. Li, Hangxin & Wang, Shengwei, 2022. "Comparative assessment of alternative MPC strategies using real meteorological data and their enhancement for optimal utilization of flexibility-resources in buildings," Energy, Elsevier, vol. 244(PA).
    13. Gianluca Serale & Luca Gnoli & Emanuele Giraudo & Enrico Fabrizio, 2021. "A Supervisory Control Strategy for Improving Energy Efficiency of Artificial Lighting Systems in Greenhouses," Energies, MDPI, vol. 14(1), pages 1-19, January.

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