Predictive control for greenhouse temperature and humidity and energy optimization by improved NMPC objective function algorithm

Persistent low temperatures in autumn and winter have a huge impact on crops, and greenhouses rely on solar radiation and heating equipment to meet the required indoor temperature. But the energy cost of frequent operation of the actuators is exceptionally high. The relationship between greenhouse environmental control accuracy and energy consumption is one of the key issues faced in greenhouse research. In this study, a non-linear model predictive control method with an improved objective function was proposed. The improved objective function used tolerance intervals and boundary constraints to optimize the objective evaluation. The nonlinear model predictive control (NMPC) controller design was based on the wavelet neural network (WNN) data-driven model and applied the interior point method to solve the optimal solution of the objective function control, thus balancing the contradiction between energy consumption and control precision. The simulation results showed that the improved NMPC method reduced energy consumption by 21.02% and 9.54% compared with the model predictive control and regular NMPC, which proved the method achieved good results in a low-temperature environment. This research can provide an important reference for the field as it offers a more efficient approach to managing greenhouse climates, potentially leading to substantial energy savings and enhanced sustainability in agricultural practices.