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

文献类型： 外文期刊

作者： Wang, Mingfei 1 ; Zheng, Wengang 2 ; Zhao, Chunjiang 1 ; Chen, Yang 1 ; Chen, Chunling 1 ; Zhang, Xin 2 ;

作者机构： 1.Shenyang Agr Univ, Coll Informat & Elect Engn, Shenyang 110866, Peoples R China

2.Beijing Acad Agr & Forestry Sci, Res Ctr Intelligent Equipment, Beijing 100097, Peoples R China

关键词： mushroom room; energy conservation; neural network; MPC

期刊名称：ENERGIES （ 影响因子：3.2； 五年影响因子：3.3 ）

ISSN：

年卷期： 2023 年 16 卷 22 期

页码：

收录情况： SCI

摘要： 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.

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