Application of dynamic programming algorithm in winter heating control of greenhouse

In order to solve the immaturity of decision-making methods in the regulation of winter heating in greenhouses, this study proposed a solution to the problem of greenhouse winter heating regulation using a dynamic programming algorithm. A mathematical model that included indoor environmental state variables, optimization decision variables, and outdoor random variables was established. The temperature is kept close to the expected value and the energy consumption is low. The model predicts the control solution by considering the cost function within the next 10 steps. The two-stage planning method was used to optimize the state of each moment step by step. The temperature control strategy model was obtained by training the relationship between indoor temperature, outdoor temperature, and heating time after optimization using a regression algorithm. Based on a typical Internet of Things (IoT) structure, the greenhouse control system was designed to regulate the optimal control according to the feedback of the current environment. Through testing and verification, the optimized control method could stabilize the temperature near the target value. Compared to the threshold control (threshold interval of 2.0°C) under similar weather conditions, the optimized control method reduced the temperature fluctuation range by 0.9°C and saved 7.83 kW·h of electricity, which is about 14.56% of the total experimental electricity consumption. This shows that the dynamic programming method is feasible for environmental regulation in actual greenhouse production, and further research can be expanded in terms of decision variables and policy models to achieve a more comprehensive, scientific, and precise regulation.