Control method of factory mushroom room air conditioning based on model predictive control

Aiming at the problems of large temperature fluctuation and high energy consumption of mushroom room air conditioning system under the traditional control mode, a Model Predictive Control（MPC）-based temperature control method for mushroom room in edible mushroom factory is proposed. Firstly, a prediction model of mushroom room resistance and capacitance temperature based on the equivalent circuit method was established, the unknown parameters in the model were identified by using Genetic Algorithms（GA）, an objective function with temperature control accuracy and system energy consumption as the optimization direction was established, and then the output of the prediction model was taken as the input of the objective function. Finally, the Particle Swarm Optimization（PSO） algorithm was used to solve the objective function and to obtain the optimal control amount in the time domain of the air conditioning system control. The experimental results showed that the MPC-based temperature control method could effectively improve the temperature control accuracy and reduce the energy consumption of the air conditioning system. The average absolute error of temperature control accuracy is reduced by 77% compared with the traditional threshold control method. In terms of running time, the MPC control method can reduce the compressor operation time by 1.2 h per day on average, and save 10.4 kWh of electric power.