Skip to main content

Digital Twin Framework for Energy Efficient Greenhouse Industry 4.0

  • Conference paper
  • First Online:
Ambient Intelligence – Software and Applications (ISAmI 2020)

Abstract

This paper introduces the ongoing research conducted on enabling industrial greenhouse growers to optimize production using multi-agent systems and digital twin technology. The project seeks to develop a production process framework for greenhouses, based on several case studies, that can be applied to different greenhouse facilities to enable a broad implementation in the industrial horticulture sector. The research will incorporate AI technology to support the production process agent in forecasting and learning optimal operating conditions within set parameters that will be feedback to the grower through a common information model. Furthermore, the production agent will communicate with other process agents to co-optimize the essential aspects of production. In turn, this allows the growers to optimize the production cost with minimal risk to product quality while aiding in upholding grid stability. The findings in this research project may be beneficial for developing industry-specific energy flexibility solutions incorporating product and process constraints.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Ma, Z., Jørgensen, B.N.: Energy flexibility of the commercial greenhouse growers: the potential and benefits of participating in the electricity market. In: 2018 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), 19–22 February 2018, pp. 1–5 (2018). https://doi.org/10.1109/isgt.2018.8403368

  2. Christensen, K., Ma, Z., Værbak, M., Demazeau, Y., Jørgensen,B.N.: Agent-based decision making for adoption of smart energy solutions. Presented at the IV international congress of research in sciences and humanities international research conference (SHIRCON 2019), Lima, Peru, 12–15 November 2019. https://doi.org/10.1109/SHIRCON48091.2019.9024880

  3. Howard, D.A., Ma, Z., Aaslyng, J.M., Jørgensen, B.N.: Data Architecture for Digital Twin of Commercial Greenhouse Production. Presented at the The 2020 RIVF international conference on computing and communication technologies, Ho Chi Minh City, Vietnam, 6–7 April 2020. https://doi.org/10.1109/RIVF48685.2020.9140726

  4. Christensen, K., Ma, Z., Demazeau, Y., Jørgensen, B.N.: Agent-based modeling for optimizing CO2 reduction in commercial greenhouse production with the implicit demand response. Presented at the The 6th IEEJ international workshop on Sensing, Actuation, Motion Control, and Optimization (SAMCON2020), Shibaura Institute of Technology, Tokyo, 14–16 March 2020. http://id.nii.ac.jp/1031/00127067/

  5. Howard, D., et al.: Optimization of energy flexibility in cooling process for brewery fermentation with multi-agent simulation. Presented at the The 6th IEEJ international workshop on Sensing, Actuation, Motion Control, and Optimization (SAMCON2020), Shibaura Institute of Technology, Tokyo, 14–16 March 2020. http://id.nii.ac.jp/1031/00127065/

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Daniel Anthony Howard .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Editor(s) (if applicable) and 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., Ma, Z., Jørgensen, B.N. (2021). Digital Twin Framework for Energy Efficient Greenhouse Industry 4.0. In: Novais, P., Vercelli, G., Larriba-Pey, J.L., Herrera, F., Chamoso, P. (eds) Ambient Intelligence – Software and Applications . ISAmI 2020. Advances in Intelligent Systems and Computing, vol 1239. Springer, Cham. https://doi.org/10.1007/978-3-030-58356-9_34

Download citation

Publish with us

Policies and ethics