Supervisory Model Predictive Control for Optimal Operation of a Greenhouse indoor environment coping with Food-Energy-Water Nexus

This paper presents a greenhouse indoor environment controller based on model predictive control (MPC), which can be integrated into existing greenhouse regulatory systems to optimally maintain critical climatic variables, including artificial lighting levels, CO2 rate, indoor temperature and humidity level within acceptable limits. The MPC based optimization problem aims to maximize the rate of crop photosynthesis while optimizing the use of the available water and energy sources, taking into account the unpredictability and intermittent nature of renewable energies and external atmospheric conditions. This would facilitate the management of greenhouses by anticipating control actions for a better quality of crops production. For that, the mathematical formulation of the optimal control problem is presented, and the numerical results related to the application of the MPC to case studies are analyzed integrating the effects of greenhouse structural considerations and the influence of climate data on its operation.