The Advancements in Agricultural Greenhouse Technologies: An Energy Management Perspective

Shaival Nagarsheth (), Kodjo Agbossou, Nilson Henao and Mathieu Bendouma
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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

Sustainability, 2025, vol. 17, issue 8, 1-30

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.

Keywords: agricultural greenhouse; microclimate; energy management; control strategies; optimization; modeling; demand response (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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