Robust Predictive Control based on Evolving Intervals for Greenhouse Energy Management

Greenhouse cultivation stands out for the ongoing food production challenge due to its ability to maintain a specific microclimate and allow crops to grow under highly variable weather conditions. Since the energy resources in greenhouses are limited, an energy management system based on evolving fuzzy prediction intervals is proposed to correctly define a proper irrigation and water extraction schedule, assuming a limited amount of energy stored. The energy management system schedules the crop irrigation to fulfill a defined daily irrigation volume while managing the water extraction according to the future photovoltaic power. In addition, evolving fuzzy prediction intervals are used to forecast the photovoltaic power and estimate its worst-case scenario from the interval's lower bound. With this information, the energy management system can be implemented using a robust model predictive controller. The proposed controller is tested assuming a reduction in the real solar power the system receives, which resembles a change in the system dynamics due to shadows and dust. Then, the controller's performance is compared against conventional fuzzy prediction intervals. Simulation results show that the proposed energy management system fulfills the reference irrigation and achieves a 33% higher state of energy and 14% higher water availability, on average, during the simulation. Thus, the proposal is better prepared for energy and water shortages due to the controller's robust approach and the model's evolving nature.