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Data-driven robust model predictive control for greenhouse temperature control and energy utilisation assessment

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  • Mahmood, Farhat
  • Govindan, Rajesh
  • Bermak, Amine
  • Yang, David
  • Al-Ansari, Tareq

Abstract

The greenhouse microclimate, especially temperature, is highly complex, and controlling it requires significant resources due to the greenhouses' inefficient design. The application of model predictive control is a promising strategy for temperature control and efficient greenhouse management. However, it does not account for the inaccuracies and uncertainties existing in the system, leading to sub-optimal temperatures. Therefore, this study proposes a comprehensive data-driven robust model predictive control framework for greenhouse temperature control and its energy utilisation assessment in the presence of uncertainties. First, an analytical model based on mass and energy balance and a data-driven model based on an artificial neural network is developed, and their prediction performance is compared. The artificial neural network demonstrates a higher prediction accuracy and is used as the system model in the proposed control framework. A robust model predictive control strategy, based on the minimax objective function and particle swarm optimisation algorithm, is developed to handle the uncertainties in the system. Results illustrate that in the presence of uncertainties, the robust model predictive control strategy outperforms the existing greenhouse climate management system and basic model predictive control with an RMSE of 0.32 °C and 0.60 °C for a two-day simulation period in winter and summer, respectively. Furthermore, the robust model predictive control strategy leads to an energy reduction of 9.67% and 23.61% in winter and summer. The proposed framework is flexible and general and can be applied to other greenhouses with different configurations and cultivated crops by fine-tuning it on the new data set.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:343:y:2023:i:c:s0306261923005548
    DOI: 10.1016/j.apenergy.2023.121190
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    References listed on IDEAS

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    1. Grewal, Harsharn S. & Maheshwari, Basant & Parks, Sophie E., 2011. "Water and nutrient use efficiency of a low-cost hydroponic greenhouse for a cucumber crop: An Australian case study," Agricultural Water Management, Elsevier, vol. 98(5), pages 841-846, March.
    2. Van Beveren, P.J.M. & Bontsema, J. & Van Straten, G. & Van Henten, E.J., 2015. "Minimal heating and cooling in a modern rose greenhouse," Applied Energy, Elsevier, vol. 137(C), pages 97-109.
    3. Farrell, Eanna & Hassan, Mohamed I. & Tufa, Ramato A. & Tuomiranta, Arttu & Avci, Ahmet H. & Politano, Antonio & Curcio, Efrem & Arafat, Hassan A., 2017. "Reverse electrodialysis powered greenhouse concept for water- and energy-self-sufficient agriculture," Applied Energy, Elsevier, vol. 187(C), pages 390-409.
    4. Kathirgamanathan, Anjukan & De Rosa, Mattia & Mangina, Eleni & Finn, Donal P., 2021. "Data-driven predictive control for unlocking building energy flexibility: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    5. 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).
    6. 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.
    7. 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).
    Full references (including those not matched with items on IDEAS)

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