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A Supervisory Control Strategy for Improving Energy Efficiency of Artificial Lighting Systems in Greenhouses

Author

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  • Gianluca Serale

    (Politecnico di Torino, Corso Duca degli Abruzzi, 24, 10129 Torino, Italy
    Andlinger Center for Energy and The Environment, Princeton University, Olden St., 86, Princeton, NJ 08540, USA)

  • Luca Gnoli

    (Politecnico di Torino, Corso Duca degli Abruzzi, 24, 10129 Torino, Italy)

  • Emanuele Giraudo

    (Freelance Innovation Consultant, 12100 Cuneo, Italy)

  • Enrico Fabrizio

    (Politecnico di Torino, Corso Duca degli Abruzzi, 24, 10129 Torino, Italy)

Abstract

Artificial lighting systems are used in commercial greenhouses to ensure year-round yields. Current Light Emitting Diode (LED) technologies improved the system efficiency. Nevertheless, having artificial lighting systems extended for hectares with power densities over 50 W / m 2 causes energy and power demand of greenhouses to be really significant. The present paper introduces an innovative supervisory and predictive control strategy to optimize the energy performance of the artificial lights of greenhouses. The controller has been implemented in a multi-span plastic greenhouse located in North Italy. The proposed control strategy has been tested on a greenhouse of 1 hectare with a lighting system with a nominal power density of 50 W m − 2 requiring an overall power supply of 1 M W for a period of 80 days. The results have been compared with the data coming from another greenhouse of 1 hectare in the same conditions implementing a state-of-the-art strategy for artificial lighting control. Results outlines that potential 19.4% cost savings are achievable. Moreover, the algorithm can be used to transform the greenhouse in a viable source of energy flexibility for grid reliability.

Suggested Citation

  • Gianluca Serale & Luca Gnoli & Emanuele Giraudo & Enrico Fabrizio, 2021. "A Supervisory Control Strategy for Improving Energy Efficiency of Artificial Lighting Systems in Greenhouses," Energies, MDPI, vol. 14(1), pages 1-19, January.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:1:p:202-:d:473940
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    References listed on IDEAS

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    Cited by:

    1. Chiara Bersani & Marco Fossa & Antonella Priarone & Roberto Sacile & Enrico Zero, 2021. "Model Predictive Control versus Traditional Relay Control in a High Energy Efficiency Greenhouse," Energies, MDPI, vol. 14(11), pages 1-21, June.
    2. 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).

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