Energy-Saving Control Method for Factory Mushroom Room Air Conditioning Based on MPC

Mingfei Wang, Wengang Zheng, Chunjiang Zhao (), Yang Chen, Chunling Chen and Xin Zhang
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Mingfei Wang: College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China
Wengang Zheng: Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
Chunjiang Zhao: College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China
Yang Chen: College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China
Chunling Chen: College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China
Xin Zhang: Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China

Energies, 2023, vol. 16, issue 22, 1-14

Abstract: The energy consumption of the mushroom room air conditioning system accounts for 40% of the total energy consumption of the mushroom factory. Efficient and energy-efficient mushroom factories and mushroom houses are the development direction of the industry. Compared with maintenance structure transformation and air conditioning equipment upgrading, energy-saving technology based on regulation methods has the advantages of less investment and fast effectiveness, which has attracted attention. The current methods for regulating air conditioning in edible mushroom factories include simple on/off thermostat control or PID. In the field of energy efficiency in commercial building air conditioning, a large number of studies have shown that compared with traditional control algorithms such as classic on/off or PID control, model predictive control can significantly improve energy efficiency. However, there is little literature mentioning the application of MPC in factory mushroom production rooms. This paper proposes a data-driven MPC and PID combined energy-saving control method for mushroom room air conditioning. This method uses the CNN-GRU-Attention combination neural network as the prediction model, combined with prediction error compensation and dynamic update mechanism of the prediction model dataset, to achieve an accurate prediction of indoor temperature in mushroom houses. Establish an objective function for air conditioning control duration and temperature, use the non-dominated sorting genetic algorithm II (NSGA-II) to solve for the optimal control sequence of the air conditioning in the control time domain, and use the entropy weight method to determine the optimal decision quantity. Integrate rolling optimization, feedback mechanism, and PID to achieve precise and energy-saving control of the mushroom room environment. The experimental results show that compared with the on/off thermostat and PID controller, the designed controller reduces power consumption by 12% and 5%, respectively, and has good application and demonstration value in the field of industrial production of edible mushrooms.

Keywords: mushroom room; energy conservation; neural network; MPC (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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