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The Advancements in Agricultural Greenhouse Technologies: An Energy Management Perspective

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  • Shaival Nagarsheth

    (Laboratoire d’Innovation et de Recherche en Énergie Intelligent (LIREI), Institut de Recherche sur L’hydrogène (IRH), Université du Québec à Trois-Rivières (UQTR), Trois-Rivières, QC G9A 5H7, Canada)

  • Kodjo Agbossou

    (Laboratoire d’Innovation et de Recherche en Énergie Intelligent (LIREI), Institut de Recherche sur L’hydrogène (IRH), Université du Québec à Trois-Rivières (UQTR), Trois-Rivières, QC G9A 5H7, Canada)

  • Nilson Henao

    (Laboratoire d’Innovation et de Recherche en Énergie Intelligent (LIREI), Institut de Recherche sur L’hydrogène (IRH), Université du Québec à Trois-Rivières (UQTR), Trois-Rivières, QC G9A 5H7, Canada)

  • Mathieu Bendouma

    (Département des sols et de Génie Agroalimentaire, Université Laval, Ville de Québec, QC G1V 0A6, Canada)

Abstract

Greenhouse technologies provide controlled environmental conditions for crop growth, often incorporating automation to enhance productivity. Energy management, which involves monitoring, controlling, and conserving energy, is particularly crucial in northern climates, where greenhouses are among the most energy-intensive sectors of agriculture. This paper presents a comprehensive review of state-of-the-art greenhouse technologies from an energy management perspective, exploring their role in enhancing efficiency and sustainability. It examines the energy management framework, key technological advancements, benefits, challenges, and available solutions in the market. Furthermore, it discusses principles and methods of energy optimization, best practices for sustainable greenhouse operations, and emerging trends in smart grids, renewable integration, and automation. Unlike previous studies primarily focusing on agricultural and control perspectives, this review highlights new insights into integrating greenhouse energy management with smart grid participation, leveraging model predictive control (MPC) for energy optimization, multi-agent reinforcement learning (DRL) for adaptive control, and digital twin technology for real-time system modeling. By bridging greenhouse energy management with transactive energy platforms, this paper underscores the importance of intelligent, data-driven decision-making in enhancing efficiency, sustainability, and system resilience while minimizing environmental impact.

Suggested Citation

  • Shaival Nagarsheth & Kodjo Agbossou & Nilson Henao & Mathieu Bendouma, 2025. "The Advancements in Agricultural Greenhouse Technologies: An Energy Management Perspective," Sustainability, MDPI, vol. 17(8), pages 1-30, April.
  • Handle: RePEc:gam:jsusta:v:17:y:2025:i:8:p:3407-:d:1632863
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    References listed on IDEAS

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    1. 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.
    2. Ajagekar, Akshay & Decardi-Nelson, Benjamin & You, Fengqi, 2024. "Energy management for demand response in networked greenhouses with multi-agent deep reinforcement learning," Applied Energy, Elsevier, vol. 355(C).
    3. Morteza Vahid-Ghavidel & Mohammad Sadegh Javadi & Matthew Gough & Sérgio F. Santos & Miadreza Shafie-khah & João P.S. Catalão, 2020. "Demand Response Programs in Multi-Energy Systems: A Review," Energies, MDPI, vol. 13(17), pages 1-17, August.
    4. Il-Seok Choi & Akhtar Hussain & Van-Hai Bui & Hak-Man Kim, 2018. "A Multi-Agent System-Based Approach for Optimal Operation of Building Microgrids with Rooftop Greenhouse," Energies, MDPI, vol. 11(7), pages 1-24, July.
    5. Ouammi, Ahmed, 2021. "Model predictive control for optimal energy management of connected cluster of microgrids with net zero energy multi-greenhouses," Energy, Elsevier, vol. 234(C).
    6. Pedro Faria & Zita Vale, 2023. "Demand Response in Smart Grids," Energies, MDPI, vol. 16(2), pages 1-3, January.
    7. van Beveren, P.J.M. & Bontsema, J. & van Straten, G. & van Henten, E.J., 2015. "Optimal control of greenhouse climate using minimal energy and grower defined bounds," Applied Energy, Elsevier, vol. 159(C), pages 509-519.
    8. 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.
    9. Theodoros Petrakis & Angeliki Kavga & Vasileios Thomopoulos & Athanassios A. Argiriou, 2022. "Neural Network Model for Greenhouse Microclimate Predictions," Agriculture, MDPI, vol. 12(6), pages 1-17, May.
    10. Yongtao Shen & Ruihua Wei & Lihong Xu, 2018. "Energy Consumption Prediction of a Greenhouse and Optimization of Daily Average Temperature," Energies, MDPI, vol. 11(1), pages 1-17, January.
    11. 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).
    12. Imani, Mahmood Hosseini & Ghadi, M. Jabbari & Ghavidel, Sahand & Li, Li, 2018. "Demand Response Modeling in Microgrid Operation: a Review and Application for Incentive-Based and Time-Based Programs," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 486-499.
    13. Georg Smolka & Ervin Kosatica & Markus Berger & Meidad Kissinger & Dor Fridman & Thomas Koellner, 2023. "Domestic water versus imported virtual blue water for agricultural production: A comparison based on energy consumption and related greenhouse gas emissions," Journal of Industrial Ecology, Yale University, vol. 27(4), pages 1123-1136, August.
    14. Golzar, Farzin & Heeren, Niko & Hellweg, Stefanie & Roshandel, Ramin, 2018. "A novel integrated framework to evaluate greenhouse energy demand and crop yield production," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 487-501.
    15. Jalal Javadi Moghaddam & Ghasem Zarei & Davood Momeni & Hamideh Faridi, 2022. "Non-linear control model for use in greenhouse climate control systems," Research in Agricultural Engineering, Czech Academy of Agricultural Sciences, vol. 68(1), pages 9-17.
    16. Juan Sebastian Roncancio & José Vuelvas & Diego Patino & Carlos Adrián Correa-Flórez, 2022. "Flower Greenhouse Energy Management to Offer Local Flexibility Markets," Energies, MDPI, vol. 15(13), pages 1-20, June.
    17. Kingsley Ukoba & Kehinde O. Olatunji & Eyitayo Adeoye & Tien-Chien Jen & Daniel M. Madyira, 2024. "Optimizing renewable energy systems through artificial intelligence: Review and future prospects," Energy & Environment, , vol. 35(7), pages 3833-3879, November.
    18. 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).
    19. Hossein Shayeghi & Elnaz Shahryari & Mohammad Moradzadeh & Pierluigi Siano, 2019. "A Survey on Microgrid Energy Management Considering Flexible Energy Sources," Energies, MDPI, vol. 12(11), pages 1-26, June.
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