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.
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References
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
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
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
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/
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/
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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
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DOI: https://doi.org/10.1007/978-3-030-58356-9_34
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