IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v356y2024ics0306261923016987.html
   My bibliography  Save this article

AI-enabled cyber-physical-biological systems for smart energy management and sustainable food production in a plant factory

Author

Listed:
  • Hu, Guoqing
  • You, Fengqi

Abstract

The advancement of controlled-environment agriculture, particularly in plant factories, offers an innovative solution to address the rising demand for food due to global population growth and urbanization. These controlled environments provide consistent and predictable crop yields, irrespective of external weather conditions, and can be tailored to achieve optimal plant growth. However, the intensive energy requirements of these systems have raised sustainability concerns. In plant factories, which provide regulated environments for sustainable food production, it remains essential to minimize energy consumption while maintaining operational efficiency. This study introduces a novel cyber-physical-biological system (CPBS) for managing energy and crop production in plant factories. The CPBS accurately captures plant biological dynamics, such as temperature, humidity, lighting, and CO2 levels, optimizes control variables, and predicts crop growth within these controlled environments. To achieve these outcomes, we leverage physics-informed deep learning (PIDL) techniques to develop high-fidelity and computationally efficient digital twins for the plant factory's internal microclimate and crop states. PIDL enables us to capture complex relationships between environmental factors and crop growth, thereby improving accuracy and decision-making in control. Using the CPBS, we optimize energy usage and resource expenses to ensure sustainable crop production rates under different daylight scenarios in the plant factory. Simulation results from a full growth cycle demonstrate that our proposed CPBS, compared to a certainty equivalent model predictive control (MPC), reduces violation cases by 84.53%. Additionally, it achieves a reduction of 13.41% and 13.04% in energy and resource usage, respectively, compared to a traditional robust MPC that considers a box-shaped uncertainty set.

Suggested Citation

  • Hu, Guoqing & You, Fengqi, 2024. "AI-enabled cyber-physical-biological systems for smart energy management and sustainable food production in a plant factory," Applied Energy, Elsevier, vol. 356(C).
    • Handle: RePEc:eee:appene:v:356:y:2024:i:c:s0306261923016987
    DOI: 10.1016/j.apenergy.2023.122334
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261923016987
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2023.122334?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. 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).
    2. Li, Bingxu & Wu, Bingjie & Peng, Yelun & Cai, Wenjian, 2022. "Tube-based robust model predictive control of multi-zone demand-controlled ventilation systems for energy saving and indoor air quality," Applied Energy, Elsevier, vol. 307(C).
    3. 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).
    4. Dong, Xing & Zhang, Chenghui & Sun, Bo, 2022. "Optimization strategy based on robust model predictive control for RES-CCHP system under multiple uncertainties," Applied Energy, Elsevier, vol. 325(C).
    5. Pizzonia, Francesco & Castiglione, Teresa & Bova, Sergio, 2016. "A Robust Model Predictive Control for efficient thermal management of internal combustion engines," Applied Energy, Elsevier, vol. 169(C), pages 555-566.
    6. Dettori, S. & Iannino, V. & Colla, V. & Signorini, A., 2018. "An adaptive Fuzzy logic-based approach to PID control of steam turbines in solar applications," Applied Energy, Elsevier, vol. 227(C), pages 655-664.
    7. Smarra, Francesco & Jain, Achin & de Rubeis, Tullio & Ambrosini, Dario & D’Innocenzo, Alessandro & Mangharam, Rahul, 2018. "Data-driven model predictive control using random forests for building energy optimization and climate control," Applied Energy, Elsevier, vol. 226(C), pages 1252-1272.
    8. Gokhale, Gargya & Claessens, Bert & Develder, Chris, 2022. "Physics informed neural networks for control oriented thermal modeling of buildings," Applied Energy, Elsevier, vol. 314(C).
    9. 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).
    10. Quan, Shengwei & Wang, Ya-Xiong & Xiao, Xuelian & He, Hongwen & Sun, Fengchun, 2021. "Feedback linearization-based MIMO model predictive control with defined pseudo-reference for hydrogen regulation of automotive fuel cells," Applied Energy, Elsevier, vol. 293(C).
    11. 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).
    12. Li, Xuan & Zhang, Wei, 2022. "Physics-informed deep learning model in wind turbine response prediction," Renewable Energy, Elsevier, vol. 185(C), pages 932-944.
    13. Di Natale, L. & Svetozarevic, B. & Heer, P. & Jones, C.N., 2022. "Physically Consistent Neural Networks for building thermal modeling: Theory and analysis," Applied Energy, Elsevier, vol. 325(C).
    14. 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.
    15. Yang, Shiyu & Wan, Man Pun & Chen, Wanyu & Ng, Bing Feng & Dubey, Swapnil, 2020. "Model predictive control with adaptive machine-learning-based model for building energy efficiency and comfort optimization," Applied Energy, Elsevier, vol. 271(C).
    16. Ishitsuka, Kazuya & Lin, Weiren, 2023. "Physics-informed neural network for inverse modeling of natural-state geothermal systems," Applied Energy, Elsevier, vol. 337(C).
    17. Qin, Chun & Zhao, Jun & Chen, Long & Liu, Ying & Wang, Wei, 2022. "An adaptive piecewise linearized weighted directed graph for the modeling and operational optimization of integrated energy systems," Energy, Elsevier, vol. 244(PA).
    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. Jiayao Liu & Linfeng Wang & Yunsheng Wang & Shipu Xu & Yong Liu, 2023. "Research on the Interface of Sustainable Plant Factory Based on Digital Twin," Sustainability, MDPI, vol. 15(6), pages 1-19, March.
    Full references (including those not matched with items on IDEAS)

    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. 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).
    2. Xiao, Tianqi & You, Fengqi, 2024. "Physically consistent deep learning-based day-ahead energy dispatching and thermal comfort control for grid-interactive communities," Applied Energy, Elsevier, vol. 353(PB).
    3. 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).
    4. Liang, Xinbin & Zhu, Xu & Chen, Siliang & Jin, Xinqiao & Xiao, Fu & Du, Zhimin, 2023. "Physics-constrained cooperative learning-based reference models for smart management of chillers considering extrapolation scenarios," Applied Energy, Elsevier, vol. 349(C).
    5. Di Natale, L. & Svetozarevic, B. & Heer, P. & Jones, C.N., 2023. "Towards scalable physically consistent neural networks: An application to data-driven multi-zone thermal building models," Applied Energy, Elsevier, vol. 340(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).
    7. Schreiber, Thomas & Netsch, Christoph & Eschweiler, Sören & Wang, Tianyuan & Storek, Thomas & Baranski, Marc & Müller, Dirk, 2021. "Application of data-driven methods for energy system modelling demonstrated on an adaptive cooling supply system," Energy, Elsevier, vol. 230(C).
    8. Xu, Wenjie & Svetozarevic, Bratislav & Di Natale, Loris & Heer, Philipp & Jones, Colin N., 2024. "Data-driven adaptive building thermal controller tuning with constraints: A primal–dual contextual Bayesian optimization approach," Applied Energy, Elsevier, vol. 358(C).
    9. Murilo Eduardo Casteroba Bento, 2024. "Load Margin Assessment of Power Systems Using Physics-Informed Neural Network with Optimized Parameters," Energies, MDPI, vol. 17(7), pages 1-20, March.
    10. Gao, Datong & Zhao, Bin & Kwan, Trevor Hocksun & Hao, Yong & Pei, Gang, 2022. "The spatial and temporal mismatch phenomenon in solar space heating applications: status and solutions," Applied Energy, Elsevier, vol. 321(C).
    11. 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).
    12. Yang, Shiyu & Wan, Man Pun, 2022. "Machine-learning-based model predictive control with instantaneous linearization – A case study on an air-conditioning and mechanical ventilation system," Applied Energy, Elsevier, vol. 306(PB).
    13. Guo, Fangzhou & Li, Ao & Yue, Bao & Xiao, Ziwei & Xiao, Fu & Yan, Rui & Li, Anbang & Lv, Yan & Su, Bing, 2024. "Improving the out-of-sample generalization ability of data-driven chiller performance models using physics-guided neural network," Applied Energy, Elsevier, vol. 354(PA).
    14. Luca Gugliermetti & Fabrizio Cumo & Sofia Agostinelli, 2024. "A Future Direction of Machine Learning for Building Energy Management: Interpretable Models," Energies, MDPI, vol. 17(3), pages 1-27, February.
    15. Zhang, Qingang & Zeng, Wei & Lin, Qinjie & Chng, Chin-Boon & Chui, Chee-Kong & Lee, Poh-Seng, 2023. "Deep reinforcement learning towards real-world dynamic thermal management of data centers," Applied Energy, Elsevier, vol. 333(C).
    16. Yang, Shiyu & Wan, Man Pun & Ng, Bing Feng & Dubey, Swapnil & Henze, Gregor P. & Chen, Wanyu & Baskaran, Krishnamoorthy, 2021. "Model predictive control for integrated control of air-conditioning and mechanical ventilation, lighting and shading systems," Applied Energy, Elsevier, vol. 297(C).
    17. 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).
    18. Liu, Xiaoqi & Lee, Seungjae & Bilionis, Ilias & Karava, Panagiota & Joe, Jaewan & Sadeghi, Seyed Amir, 2021. "A user-interactive system for smart thermal environment control in office buildings," Applied Energy, Elsevier, vol. 298(C).
    19. Wang, Yi & Qiu, Dawei & Sun, Mingyang & Strbac, Goran & Gao, Zhiwei, 2023. "Secure energy management of multi-energy microgrid: A physical-informed safe reinforcement learning approach," Applied Energy, Elsevier, vol. 335(C).
    20. Ge, Quanwu & Ke, Zhixin & Liu, Yutong & Chai, Fu & Yang, Wenhua & Zhang, Zhili & Wang, Yang, 2023. "Low-carbon strategy of demand-based regulating heating and lighting for the heterogeneous environment in beijing Venlo-type greenhouse," Energy, Elsevier, vol. 267(C).

    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:appene:v:356:y:2024:i:c:s0306261923016987. 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/405891/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.