Research on Energy-Saving Optimization of Mushroom Growing Control Room Based on Neural Network Model Predictive Control

Yifan Song, Wengang Zheng, Guoqiang Guo, Mingfei Wang (), Changshou Luo, Cheng Chen and Zuolin Li ()
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Yifan Song: College of Agricultural Engineering, Shanxi Agricultural University, Jinzhong 030801, China
Wengang Zheng: College of Agricultural Engineering, Shanxi Agricultural University, Jinzhong 030801, China
Guoqiang Guo: Intelligent Equipment Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
Mingfei Wang: Intelligent Equipment Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
Changshou Luo: Institute of Data Science and Agricultural Economics, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
Cheng Chen: Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
Zuolin Li: Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China

Energies, 2025, vol. 18, issue 20, 1-24

Abstract: In the heating, ventilation, and air conditioning (HVAC) systems of mushroom growing control rooms, traditional rule-based control methods are commonly adopted. However, these methods are characterized by response delays, leading to underutilization of energy-saving potential and energy costs that constitute a disproportionately high share of overall production costs. Therefore, minimizing the running time of the air conditioning system is crucial while maintaining the optimal growing environment for mushrooms. To address the aforementioned issues, this paper proposed a sensor optimization method based on the combination of principal component analysis (PCA) and information entropy. Furthermore, model predictive control (MPC) was implemented using a gated recurrent unit (GRU) neural network with an attention mechanism (GRU-Attention) as the prediction model to optimize the air conditioning system. First, a method combining PCA and information entropy was proposed to select the three most representative sensors from the 16 sensors in the mushroom room, thus eliminating redundant information and correlations. Then, a temperature prediction model based on GRU-Attention was adopted, with its hyperparameters optimized using the Optuna framework. Finally, an improved crayfish optimization algorithm (ICOA) was proposed as an optimizer for MPC. Its objective was to solve the control sequence with high accuracy and low energy consumption. The average energy consumption was reduced by approximately 11.2%, achieving a more stable temperature control effect.

Keywords: mushroom room; sensor optimization; model predictive control; HVAC system; crayfish optimization algorithm (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: 2025
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