Greenhouse Climate Setpoint Optimization: An Online Decision Strategy

Since the greenhouse climate setpoint greatly impacts the energy consumption and crop yield, optimizing the setpoint can significantly improve the energy efficiency. However, the great uncertainty of the weather makes such optimization problem be difficult to solve. Therefore, how to handle the great uncertainty becomes a challenge for setpoint optimization. To solve this problem, this work proposes an online receding horizon multi-objective optimization method to make a trade-off between the total energy consumption and the final crop yield to obtain a set of good greenhouse climate setpoints. The proposed method uses a surrogate assisted multi-objective optimization algorithm to minimize the energy consumption and maximize the crop yield to obtain the optimal mean temperatures of the crop development stages. Since the proposed method does not directly optimize the setpoint, a serialization method is proposed to transform the mean daily temperature into the setpoint of the inside temperature. In addition, since daily predicted weather is usually rough, this work developed an interpolation method for weather, which was validated using real weather data collected in a Venlo-type greenhouse. The proposed greenhouse climate setpoint optimization method was compared with the Priva system, and the results indicate the advantage of the proposed method.

Automated summary

Greenhouse climate setpoint optimization is challenging due to the great uncertainty of weather. This study proposes an online receding horizon multi-objective optimization method and addresses the challenge with surrogate/interpolation methods. The proposed method shows advantages over the traditional Priva system based on real greenhouse data.