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

• Main Authors: Gianluca Serale, Luca Gnoli, Emanuele Giraudo, Enrico Fabrizio
• Format: Article
• Language: English
• Published: MDPI AG 2021-01-01
• Series: Energies
• Subjects: greenhouse; artificial lighting system; natural lighting; energy flexibility; predictive control; optimization
• Online Access: https://www.mdpi.com/1996-1073/14/1/202
• ISSN: 1996-1073

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 <inline-formula><math display="inline"><semantics><mrow><mn>50</mn><mspace width="4pt"></mspace><mi mathvariant="normal">W</mi><mo>/</mo><msup><mi mathvariant="normal">m</mi><mn>2</mn></msup></mrow></semantics></math></inline-formula> 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 <inline-formula><math display="inline"><semantics><mi mathvariant="normal">W</mi></semantics></math></inline-formula><inline-formula><math display="inline"><semantics><msup><mi mathvariant="normal">m</mi><mrow><mo>−</mo><mn>2</mn></mrow></msup></semantics></math></inline-formula> requiring an overall power supply of 1 <inline-formula><math display="inline"><semantics><mi mathvariant="normal">M</mi></semantics></math></inline-formula><inline-formula><math display="inline"><semantics><mi mathvariant="normal">W</mi></semantics></math></inline-formula> 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.