IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v168y2022ics1364032122006748.html
   My bibliography  Save this article

Renewable energy-powered semi-closed greenhouse for sustainable crop production using model predictive control and machine learning for energy management

Author

Listed:
  • Hu, Guoqing
  • You, Fengqi

Abstract

Renewable energy consumption in agriculture is ascending, catering to the food needs of the rising population and protecting the environment. Maximizing renewable energy usage efficiency is essential for achieving sustainable development goals. In this work, a nonlinear integrated controlled environment agriculture model is constructed to correlate the impact of weather disturbances, temperature control actuators, humidity control actuators, fertilization, and irrigation to the states of crop production facilities and crop growing conditions. Linearization of the model is performed to reduce the computation time while retaining the accuracy of the nonlinear model. A robust model predictive control framework is developed to maximize renewable energy power usage efficiency and maintain a hospitable sustainable cultivation environment. To improve the robustness of control to hedge against the forecast uncertainties, the disjunctive data-driven uncertainty sets built upon the historical forecast errors are constructed by using machine learning methods, including principal component analysis and kernel density estimation. This work also presents the result of the simulation of controlling a renewable energy-powered semi-closed greenhouse growing tomatoes located in Ithaca, New York. Compared to other model predictive control frameworks, which do not leverage the machine learning approaches, the proposed control framework enables a 0.7%–66.9% reduction in renewable energy usage and a 0.7 wt% to 16.1 wt% increase in crop production. Further analysis of this work reveals that the integrated controlled environment agriculture model can help in increasing renewable energy usage efficiency from 4.7% to 127.5%.

Suggested Citation

  • Hu, Guoqing & You, Fengqi, 2022. "Renewable energy-powered semi-closed greenhouse for sustainable crop production using model predictive control and machine learning for energy management," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    • Handle: RePEc:eee:rensus:v:168:y:2022:i:c:s1364032122006748
    DOI: 10.1016/j.rser.2022.112790
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032122006748
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.rser.2022.112790?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Bhowmik, Chiranjib & Bhowmik, Sumit & Ray, Amitava & Pandey, Krishna Murari, 2017. "Optimal green energy planning for sustainable development: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 71(C), pages 796-813.
    2. Canakci, Murad & Yasemin Emekli, N. & Bilgin, Sefai & Caglayan, Nuri, 2013. "Heating requirement and its costs in greenhouse structures: A case study for Mediterranean region of Turkey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 24(C), pages 483-490.
    3. Cuce, Erdem & Harjunowibowo, Dewanto & Cuce, Pinar Mert, 2016. "Renewable and sustainable energy saving strategies for greenhouse systems: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 34-59.
    4. Gallardo, M. & Giménez, C. & Martínez-Gaitán, C. & Stöckle, C.O. & Thompson, R.B. & Granados, M.R., 2011. "Evaluation of the VegSyst model with muskmelon to simulate crop growth, nitrogen uptake and evapotranspiration," Agricultural Water Management, Elsevier, vol. 101(1), pages 107-117.
    5. Dayan, E. & van Keulen, H. & Jones, J. W. & Zipori, I. & Shmuel, D. & Challa, H., 1993. "Development, calibration and validation of a greenhouse tomato growth model: II. Field calibration and validation," Agricultural Systems, Elsevier, vol. 43(2), pages 165-183.
    6. Zhao, Ning & You, Fengqi, 2022. "Sustainable power systems operations under renewable energy induced disjunctive uncertainties via machine learning-based robust optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    7. Dayan, E. & van Keulen, H. & Jones, J. W. & Zipori, I. & Shmuel, D. & Challa, H., 1993. "Development, calibration and validation of a greenhouse tomato growth model: I. Description of the model," Agricultural Systems, Elsevier, vol. 43(2), pages 145-163.
    8. Achour, Yasmine & Ouammi, Ahmed & Zejli, Driss, 2021. "Technological progresses in modern sustainable greenhouses cultivation as the path towards precision agriculture," Renewable and Sustainable Energy Reviews, Elsevier, vol. 147(C).
    9. 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).
    10. Cossu, Marco & Cossu, Andrea & Deligios, Paola A. & Ledda, Luigi & Li, Zhi & Fatnassi, Hicham & Poncet, Christine & Yano, Akira, 2018. "Assessment and comparison of the solar radiation distribution inside the main commercial photovoltaic greenhouse types in Europe," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 822-834.
    11. Hassanien, Reda Hassanien Emam & Li, Ming & Dong Lin, Wei, 2016. "Advanced applications of solar energy in agricultural greenhouses," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 989-1001.
    12. Chauhan, Prashant Singh & Kumar, Anil & Gupta, Bhupendra, 2017. "A review on thermal models for greenhouse dryers," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 548-558.
    13. 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.
    14. Panwar, N.L. & Kaushik, S.C. & Kothari, Surendra, 2011. "Solar greenhouse an option for renewable and sustainable farming," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(8), pages 3934-3945.
    15. Evans, Annette & Strezov, Vladimir & Evans, Tim J., 2009. "Assessment of sustainability indicators for renewable energy technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(5), pages 1082-1088, June.
    16. Lei He & Bo Lei & Haiquan Bi & Tao Yu, 2016. "Simplified Building Thermal Model Used for Optimal Control of Radiant Cooling System," Mathematical Problems in Engineering, Hindawi, vol. 2016, pages 1-15, February.
    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. Hu, Guoqing & You, Fengqi, 2023. "An AI framework integrating physics-informed neural network with predictive control for energy-efficient food production in the built environment," Applied Energy, Elsevier, vol. 348(C).
    2. Ashok Bhansali & Namala Narasimhulu & Rocío Pérez de Prado & Parameshachari Bidare Divakarachari & Dayanand Lal Narayan, 2023. "A Review on Sustainable Energy Sources Using Machine Learning and Deep Learning Models," Energies, MDPI, vol. 16(17), pages 1-18, August.
    3. 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).
    4. Yang, Shiyu & Oliver Gao, H. & You, Fengqi, 2022. "Model predictive control for Demand- and Market-Responsive building energy management by leveraging active latent heat storage," Applied Energy, Elsevier, vol. 327(C).
    5. Xiao, Tianqi & You, Fengqi, 2023. "Building thermal modeling and model predictive control with physically consistent deep learning for decarbonization and energy optimization," Applied Energy, Elsevier, vol. 342(C).
    6. Chen, Wei-Han & You, Fengqi, 2022. "Sustainable building climate control with renewable energy sources using nonlinear model predictive control," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).

    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. 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.
    2. 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).
    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. Wu, Xiaoyang & Li, Yiming & Jiang, Lingling & Wang, Yang & Liu, Xingan & Li, Tianlai, 2023. "A systematic analysis of multiple structural parameters of Chinese solar greenhouse based on the thermal performance," Energy, Elsevier, vol. 273(C).
    5. Javier Padilla & Carlos Toledo & Rodolfo López-Vicente & Raquel Montoya & José-Ramón Navarro & José Abad & Antonio Urbina, 2021. "Passive Heating and Cooling of Photovoltaic Greenhouses Including Thermochromic Materials," Energies, MDPI, vol. 14(2), pages 1-22, January.
    6. Wang, Tianyue & Wu, Gaoxiang & Chen, Jiewei & Cui, Peng & Chen, Zexi & Yan, Yangyang & Zhang, Yan & Li, Meicheng & Niu, Dongxiao & Li, Baoguo & Chen, Hongyi, 2017. "Integration of solar technology to modern greenhouse in China: Current status, challenges and prospect," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 1178-1188.
    7. Yano, Akira & Cossu, Marco, 2019. "Energy sustainable greenhouse crop cultivation using photovoltaic technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 109(C), pages 116-137.
    8. Carlos Toledo & Alessandra Scognamiglio, 2021. "Agrivoltaic Systems Design and Assessment: A Critical Review, and a Descriptive Model towards a Sustainable Landscape Vision (Three-Dimensional Agrivoltaic Patterns)," Sustainability, MDPI, vol. 13(12), pages 1-38, June.
    9. Dayan, E. & Presnov, E. & Fuchs, M., 2004. "Prediction and calculation of morphological characteristics and distribution of assimilates in the ROSGRO model," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 65(1), pages 101-116.
    10. La Notte, Luca & Giordano, Lorena & Calabrò, Emanuele & Bedini, Roberto & Colla, Giuseppe & Puglisi, Giovanni & Reale, Andrea, 2020. "Hybrid and organic photovoltaics for greenhouse applications," Applied Energy, Elsevier, vol. 278(C).
    11. Sellak, Hamza & Ouhbi, Brahim & Frikh, Bouchra & Palomares, Iván, 2017. "Towards next-generation energy planning decision-making: An expert-based framework for intelligent decision support," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 1544-1577.
    12. Oreggioni, G.D. & Luberti, M. & Tassou, S.A., 2019. "Agricultural greenhouse CO2 utilization in anaerobic-digestion-based biomethane production plants: A techno-economic and environmental assessment and comparison with CO2 geological storage," Applied Energy, Elsevier, vol. 242(C), pages 1753-1766.
    13. Milad Kolagar & Seyed Mohammad Hassan Hosseini & Ramin Felegari & Parviz Fattahi, 2020. "Policy-making for renewable energy sources in search of sustainable development: a hybrid DEA-FBWM approach," Environment Systems and Decisions, Springer, vol. 40(4), pages 485-509, December.
    14. Cossu, Marco & Cossu, Andrea & Deligios, Paola A. & Ledda, Luigi & Li, Zhi & Fatnassi, Hicham & Poncet, Christine & Yano, Akira, 2018. "Assessment and comparison of the solar radiation distribution inside the main commercial photovoltaic greenhouse types in Europe," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 822-834.
    15. Zhang, Guanshan & Ding, Xiaoming & Li, Tianhua & Pu, Wenyang & Lou, Wei & Hou, Jialin, 2020. "Dynamic energy balance model of a glass greenhouse: An experimental validation and solar energy analysis," Energy, Elsevier, vol. 198(C).
    16. Kazuya Maeda & Dong-Hyuk Ahn, 2021. "Estimation of Dry Matter Production and Yield Prediction in Greenhouse Cucumber without Destructive Measurements," Agriculture, MDPI, vol. 11(12), pages 1-10, November.
    17. 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).
    18. Li, Zhi & Yano, Akira & Yoshioka, Hidekazu, 2020. "Feasibility study of a blind-type photovoltaic roof-shade system designed for simultaneous production of crops and electricity in a greenhouse," Applied Energy, Elsevier, vol. 279(C).
    19. Zhi Li & Akira Yano & Marco Cossu & Hidekazu Yoshioka & Ichiro Kita & Yasuomi Ibaraki, 2018. "Electrical Energy Producing Greenhouse Shading System with a Semi-Transparent Photovoltaic Blind Based on Micro-Spherical Solar Cells," Energies, MDPI, vol. 11(7), pages 1-23, June.
    20. Nursyahirah Mohd Shatar & Mohd Azizi Abdul Rahman & Mohd Nabil Muhtazaruddin & Sheikh Ahmad Zaki Shaikh Salim & Baljit Singh & Firdaus Muhammad-Sukki & Nurul Aini Bani & Ahmad Shakir Mohd Saudi & Jorg, 2019. "Performance Evaluation of Unconcentrated Photovoltaic-Thermoelectric Generator Hybrid System under Tropical Climate," Sustainability, MDPI, vol. 11(22), pages 1-21, November.

    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:eee:rensus:v:168:y:2022:i:c:s1364032122006748. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/600126/description#description .

    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.