IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i11p3353-d570622.html
   My bibliography  Save this article

Model Predictive Control versus Traditional Relay Control in a High Energy Efficiency Greenhouse

Author

Listed:
  • Chiara Bersani

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

  • Marco Fossa

    (Department Mechanical, Energy, Management and Transportation Engineering, University of Genoa, 16145 Genova, Italy)

  • Antonella Priarone

    (Department Mechanical, Energy, Management and Transportation Engineering, University of Genoa, 16145 Genova, Italy)

  • Roberto Sacile

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

  • Enrico Zero

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

Abstract

The sustainable agriculture cultivation in greenhouses is constantly evolving thanks to new technologies and methodologies able to improve the crop yield and to solve the common concerns which occur in protected environments. In this paper, an MPC-based control system has been realized in order to control the indoor air temperature in a high efficiency greenhouse. The main objective is to determine the optimal control signals related to the water mass flow rate supplied by a heat pump. The MPC model allows a predefined temperature profile to be tracked with an energy saving approach. The MPC has been implemented as a multiobjective optimization model that takes into account the dynamic behavior of the greenhouse in terms of energy and mass balances. The energy supply is provided by a ground coupled heat pump (GCHP) and by the solar radiation while the energy losses related to heat transfers across the glazed envelope. The proposed MPC method was applied in a smart innovative greenhouse located in Italy, and its performances were compared with a traditional reactive control method in terms of deviation of the indoor temperature in respect to the desired one and in terms of electric power consumption. The results demonstrated that, for a time horizon of 20 h, in a greenhouse with dimensions 15.3 and 9.9 m and an average height of 4.5 m, the proposed MPC approach saved about 30% in electric power compared with a relay control, guaranteeing a consistent and reliable temperature profile in respect to the predefined tracked one.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:11:p:3353-:d:570622
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/11/3353/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/11/3353/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Vadiee, Amir & Martin, Viktoria, 2012. "Energy management in horticultural applications through the closed greenhouse concept, state of the art," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(7), pages 5087-5100.
    2. Iddio, E. & Wang, L. & Thomas, Y. & McMorrow, G. & Denzer, A., 2020. "Energy efficient operation and modeling for greenhouses: A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 117(C).
    3. Kalliopi Tataraki & Eugenia Giannini & Konstantinos Kavvadias & Zacharias Maroulis, 2020. "Cogeneration Economics for Greenhouses in Europe," Energies, MDPI, vol. 13(13), pages 1-27, July.
    4. Martin Vrlić & Daniel Ritzberger & Stefan Jakubek, 2021. "Model-Predictive-Control-Based Reference Governor for Fuel Cells in Automotive Application Compared with Performance from a Real Vehicle," Energies, MDPI, vol. 14(8), pages 1-17, April.
    5. Boccalatte, A. & Fossa, M. & Ménézo, C., 2020. "Best arrangement of BIPV surfaces for future NZEB districts while considering urban heat island effects and the reduction of reflected radiation from solar façades," Renewable Energy, Elsevier, vol. 160(C), pages 686-697.
    6. Adnan Rasheed & Wook Ho Na & Jong Won Lee & Hyeon Tae Kim & Hyun Woo Lee, 2019. "Optimization of Greenhouse Thermal Screens for Maximized Energy Conservation," Energies, MDPI, vol. 12(19), pages 1-20, September.
    7. Carson Kinney & Alireza Dehghani-Sanij & SeyedBijan Mahbaz & Maurice B. Dusseault & Jatin S. Nathwani & Roydon A. Fraser, 2019. "Geothermal Energy for Sustainable Food Production in Canada’s Remote Northern Communities," Energies, MDPI, vol. 12(21), pages 1-25, October.
    8. Luis Gabriel Marín & Mark Sumner & Diego Muñoz-Carpintero & Daniel Köbrich & Seksak Pholboon & Doris Sáez & Alfredo Núñez, 2019. "Hierarchical Energy Management System for Microgrid Operation Based on Robust Model Predictive Control," Energies, MDPI, vol. 12(23), pages 1-19, November.
    9. 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.
    10. Hyung-Kweon Kim & Young-Sun Ryou & Young-Hwa Kim & Tae-Seok Lee & Sung-Sik Oh & Yong-Hyeon Kim, 2021. "Estimating the Thermal Properties of the Cover and the Floor in a Plastic Greenhouse," Energies, MDPI, vol. 14(7), pages 1-11, April.
    11. Ruixin Lv & Zhongyuan Yuan & Bo Lei & Jiacheng Zheng & Xiujing Luo, 2021. "Model Predictive Control with Adaptive Building Model for Heating Using the Hybrid Air-Conditioning System in a Railway Station," Energies, MDPI, vol. 14(7), pages 1-22, April.
    12. 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.
    13. Lioutas, Evagelos D. & Charatsari, Chrysanthi, 2021. "Enhancing the ability of agriculture to cope with major crises or disasters: What the experience of COVID-19 teaches us," Agricultural Systems, Elsevier, vol. 187(C).
    14. Ouazzani Chahidi, Laila & Fossa, Marco & Priarone, Antonella & Mechaqrane, Abdellah, 2021. "Energy saving strategies in sustainable greenhouse cultivation in the mediterranean climate – A case study," Applied Energy, Elsevier, vol. 282(PA).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Gustavo Cevallos & Marco Herrera & Ramon Jaimez & Hanna Aboukheir & Oscar Camacho, 2022. "A Practical Hybrid Control Approach for a Greenhouse Microclimate: A Hardware-in-the-Loop Implementation," Agriculture, MDPI, vol. 12(11), pages 1-28, November.
    2. Beatrice Faniyi & Zhenhua Luo, 2023. "A Physics-Based Modelling and Control of Greenhouse System Air Temperature Aided by IoT Technology," Energies, MDPI, vol. 16(6), pages 1-18, March.
    3. Aurora González-Vidal & José Mendoza-Bernal & Alfonso P. Ramallo & Miguel Ángel Zamora & Vicente Martínez & Antonio F. Skarmeta, 2022. "Smart Operation of Climatic Systems in a Greenhouse," Agriculture, MDPI, vol. 12(10), pages 1-18, October.
    4. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Katzin, David & van Henten, Eldert J. & van Mourik, Simon, 2022. "Process-based greenhouse climate models: Genealogy, current status, and future directions," Agricultural Systems, Elsevier, vol. 198(C).
    2. Golmohamadi, Hessam, 2022. "Demand-side management in industrial sector: A review of heavy industries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    3. 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.
    4. Rabiu, Anis & Adesanya, Misbaudeen Aderemi & Na, Wook-Ho & Ogunlowo, Qazeem O. & Akpenpuun, Timothy D. & Kim, Hyeon Tae & Lee, Hyun-Woo, 2023. "Thermal performance and energy cost of Korean multispan greenhouse energy-saving screens," Energy, Elsevier, vol. 285(C).
    5. 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.
    6. 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).
    7. Zhang, Menghang & Yan, Tingxiang & Wang, Wei & Jia, Xuexiu & Wang, Jin & Klemeš, Jiří Jaromír, 2022. "Energy-saving design and control strategy towards modern sustainable greenhouse: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 164(C).
    8. Costantino, Andrea & Comba, Lorenzo & Sicardi, Giacomo & Bariani, Mauro & Fabrizio, Enrico, 2021. "Energy performance and climate control in mechanically ventilated greenhouses: A dynamic modelling-based assessment and investigation," Applied Energy, Elsevier, vol. 288(C).
    9. 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.
    10. 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.
    11. 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.
    12. Morice R. O. Odhiambo & Adnan Abbas & Xiaochan Wang & Ehsan Elahi, 2020. "Thermo-Environmental Assessment of a Heated Venlo-Type Greenhouse in the Yangtze River Delta Region," Sustainability, MDPI, vol. 12(24), pages 1-34, December.
    13. Temiz, Mert & Dincer, Ibrahim, 2022. "A unique ocean and solar based multigenerational system with hydrogen production and thermal energy storage for Arctic communities," Energy, Elsevier, vol. 239(PB).
    14. Zhao, Jing & Yang, Zilan & Shi, Linyu & Liu, Dehan & Li, Haonan & Mi, Yumiao & Wang, Hongbin & Feng, Meili & Hutagaol, Timothy Joseph, 2024. "Photovoltaic capacity dynamic tracking model predictive control strategy of air-conditioning systems with consideration of flexible loads," Applied Energy, Elsevier, vol. 356(C).
    15. Luo, X.J. & Oyedele, Lukumon O. & Ajayi, Anuoluwapo O. & Akinade, Olugbenga O. & Owolabi, Hakeem A. & Ahmed, Ashraf, 2020. "Feature extraction and genetic algorithm enhanced adaptive deep neural network for energy consumption prediction in buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    16. Barkat Rabbi & Zhong-Hua Chen & Subbu Sethuvenkatraman, 2019. "Protected Cropping in Warm Climates: A Review of Humidity Control and Cooling Methods," Energies, MDPI, vol. 12(14), pages 1-24, July.
    17. Premaratne Samaranayake & Weiguang Liang & Zhong-Hua Chen & David Tissue & Yi-Chen Lan, 2020. "Sustainable Protected Cropping: A Case Study of Seasonal Impacts on Greenhouse Energy Consumption during Capsicum Production," Energies, MDPI, vol. 13(17), pages 1-23, August.
    18. Algirdas Justinas Staugaitis & Bernardas Vaznonis, 2022. "Short-Term Speculation Effects on Agricultural Commodity Returns and Volatility in the European Market Prior to and during the Pandemic," Agriculture, MDPI, vol. 12(5), pages 1-26, April.
    19. Deng, Xiangtian & Zhang, Yi & Jiang, Yi & Zhang, Yi & Qi, He, 2024. "A novel operation method for renewable building by combining distributed DC energy system and deep reinforcement learning," Applied Energy, Elsevier, vol. 353(PB).
    20. Urbano, Eva M. & Martinez-Viol, Victor & Kampouropoulos, Konstantinos & Romeral, Luis, 2022. "Risk assessment of energy investment in the industrial framework – Uncertainty and Sensitivity Analysis for energy design and operation optimisation," Energy, Elsevier, vol. 239(PA).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:14:y:2021:i:11:p:3353-:d:570622. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.