Skip to main content

Data-Driven Digital Twin Based Energy Flexibility Investigation for Commercial Greenhouse Production Process

  • Conference paper
  • First Online:
Progress in Artificial Intelligence (EPIA 2024)

Abstract

Commercial greenhouses require sophisticated energy management strategies to ensure optimal plant growth while minimizing operational costs. The ability to adapt energy usage flexibly in response to varying market prices and environmental conditions is crucial. This paper introduces a data-driven digital twin, incorporating discrete event simulations and symbolic regression, to model and optimize real-world processes within a commercial greenhouse. This approach enables precise control and adjustment of energy usage, enhancing flexibility. A case study of one of Denmark's largest commercial greenhouses is applied to demonstrate the digital twin's applicability and effectiveness. Implementation of the digital twin significantly enhanced the greenhouse's energy flexibility, allowing for adaptive energy consumption that aligns with fluctuating energy prices and availability without compromising plant growth. The results illustrate that digital twins can substantially improve energy flexibility, providing a valuable tool for greenhouse operators to optimize energy usage dynamically.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Howard, D.A., Ma, Z., Jørgensen, B.N.: Evaluation of industrial energy flexibility potential: a scoping review. In: 2021 22nd IEEE International Conference on Industrial Technology (ICIT) (2021)

    Google Scholar 

  2. Koulouris, A., Misailidis, N., Petrides, D.: Applications of process and digital twin models for production simulation and scheduling in the manufacturing of food ingredients and products. Food Bioprod. Process. 126, 317–333 (2021)

    Article  Google Scholar 

  3. Tsubo, M., Walker, S.: Relationships between photosynthetically active radiation and clearness index at Bloemfontein, South Africa. Theoret. Appl. Climatol. 80(1), 17–25 (2004)

    Article  Google Scholar 

  4. Choi, B.K., Kang, D., Modeling and Simulation of Discrete-Event Systems: Wiley. Hoboken (2013)

    Google Scholar 

  5. Qiao, D., Wang, Y.: A review of the application of discrete event simulation in manufacturing. In: IOP Conference Series: Earth and Environmental Science. IOP Publishing Ltd. (2021)

    Google Scholar 

  6. Yang, S.L., et al.: Verification of intelligent scheduling based on deep reinforcement learning for distributed workshops via discrete event simulation. Adv. Prod. Eng. Manage. 17(4), 401–412 (2022)

    Google Scholar 

  7. Aliunir, F., Zagloel, T.Y.M., Ardi, R.: Discrete-event simulation and optimization of spare parts inventory and preventive maintenance integration model considering cooling down and machine dismantling time factor. Evergreen 7(1), 79–85 (2020)

    Article  Google Scholar 

  8. Rueda Delgado, R., et al.: A comparison between NARX neural networks and symbolic regression: an application for energy consumption forecasting. In: Medina, J., Ojeda-Aciego, M., Verdegay, J., Perfilieva, I., Bouchon-Meunier, B., Yager, R. (eds.) IPMU 2018. CCIS, vol. 855, pp. 16–27. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-91479-4_2

  9. Kabliman, E., et al.: Application of symbolic regression for constitutive modeling of plastic deformation. Appl. Eng. Sci. 6, 100052 (2021)

    Google Scholar 

  10. Mridula, M.R., Nair, A.S., Kumar, K.S.: Genetic programming based models in plant tissue culture: an addendum to traditional statistical approach. PLoS Comput. Biol. 14(2), e1005976 (2018)

    Article  Google Scholar 

  11. Kantor, D., Zuben, F.J.V., Franca, F.O.d.: Simulated annealing for symbolic regression. In: Proceedings of the Genetic and Evolutionary Computation Conference, Lille, France, pp. 592–599. Association for Computing Machinery (2021)

    Google Scholar 

  12. Ashok, D., et al.: Logic Guided Genetic Algorithms (2020)

    Google Scholar 

  13. Angizeh, F., et al.: Optimal production scheduling for smart manufacturers with application to food production planning. Comput. Electr. Eng. 84, 106609 (2020)

    Google Scholar 

  14. Madoumier, M., et al.: Towards a holistic approach for multi-objective optimization of food processes: a critical review. Trends Food Sci. Technol. 86, 1–15 (2019)

    Article  Google Scholar 

  15. Jafferali, M., Venkateshwaran, J., Son, Y.-J.: Performance comparison of search‐based simulation optimisation algorithms for operations scheduling. Int. J. Simul. Process Modell. 1, 58–71 (2005)

    Google Scholar 

  16. Eskandari, H., et al.: Performance analysis of commercial simulation-based optimization packages: OptQuest and wittness optimizer. In: Proceedings - Winter Simulation Conference, pp. 2358–2368 (2011)

    Google Scholar 

  17. McCree, K.J.: The action spectrum, absorptance and quantum yield of photosynthesis in crop plants. Agric. Meteorol. 9, 191–216 (1971)

    Article  Google Scholar 

  18. Rhodes, B.: ephem (2022). https://pypi.org/project/ephem/. Accessed 26 July 2023

  19. Qu, Y.: A Digital Twin Framework for Commercial Greenhouse Climate Control System, in Center for Energy Informatics, University of Southern Denmark: Syddansk Universitet. Det Tekniske Fakultet (2023)

    Google Scholar 

Download references

Acknowledgments

The work is part of the Greenhouse Industry 4.0 project funded by the Energy Technology Development and Demonstration Program (64019-0018), and part of Project “IEA IETS Annex Task XVIII - Digitization, artificial intelligence and related technologies for energy efficiency and reduction of greenhouse gas emissions in industry” funded by EUDP (Case no.134-21010).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Zheng Ma .

Editor information

Editors and Affiliations

Ethics declarations

The authors have no competing interests to declare that are relevant to the content of this article.

Rights and permissions

Reprints and permissions

Copyright information

© 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Howard, D.A., Værbak, M., Ma, Z., Jørgensen, B.N., Ma, Z. (2025). Data-Driven Digital Twin Based Energy Flexibility Investigation for Commercial Greenhouse Production Process. In: Santos, M.F., Machado, J., Novais, P., Cortez, P., Moreira, P.M. (eds) Progress in Artificial Intelligence. EPIA 2024. Lecture Notes in Computer Science(), vol 14967. Springer, Cham. https://doi.org/10.1007/978-3-031-73497-7_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-73497-7_16

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-73496-0

  • Online ISBN: 978-3-031-73497-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics