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

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 50W/m2 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 Wm−2 requiring an overall power supply of 1 MW 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.

Model Predictive Control versus Traditional Relay Control in a High Energy Efficiency Greenhouse

“…In reference [66], the authors presented a MPC controller able to maximize the crop photosynthesis acting on the water and energy management, while, in reference [67], the MPC adoption in an irrigation system reduced 30% of the water consumption over a period of three weeks. Serale et al in reference [68], implemented a MPC strategy to control the artificial lighting system to optimize the energy efficiency. In reference [69], a MPC approach was used to control the air temperature in a greenhouse managing two variables associated with heating pipe temperature and the air volumetric flow rate (windows opening).…”

Load Profile Optimization Using Electricity Wholesale Market Price Data for Discrete Manufacturing

“…Lowering the overall energy uptake is of the essence within the EDS too, but unclaimed flexibilities on the demand side considering production systems of DMM are more easily accessible. Rather than lowering the energy uptake per se these may be utilized in order to react to the growing share of VRE on the supply side and thus, to mitigate costs [3,4]. This is in opposition to most processes within the EIS, where continuous processes cannot be interrupted.…”

Monitoring System for the Management of the Common Agricultural Policy Using Machine Learning and Remote Sensing

“…Today, different research projects promote the use of AI, of which we highlight the following: NaLamKI [11], which aims to promote more efficient and sustainable agriculture by developing artificial intelligence methods to analyse remote sensing data for modelling agricultural processes and for 5G networks on farms, or knowlEdge [12], which proposes, among other things, a knowledge-based platform to distribute and exchange trained AI models. Other, more concrete, examples allow us to solve needs in the agricultural sector: reducing the environmental impact of pesticides in pest management [13] or controlling the artificial lighting system to optimise the energy efficiency of greenhouses [14].…”