基于模型预测控制的菇房空调节能控制方法  

Energy-saving Control Method of Air Conditioning in Mushroom HouseBased on Model Predictive Control

作　　者：张馨 孔祥书[1,2] 郑文刚 王明飞[2,3] 单飞飞[2] 鲍峰 ZHANG Xin;KONG Xiangshu;ZHENG Wen'gang;WANG Mingfei;SHAN Feifei;BAO Feng(College of Mechanical and Electrical Engineering,Xinjiang Agricultural University,Urumqi 830052,China;Intelligent Equipment Technology Research Center,Beijing Academy of Agriculure and Forestry Sciences,Bejing 100097,China;North China Urban Agriculture Key Laboratory,Ministry of Agriculture and Rural Affairs,Bejing 100096,China)

机构地区：[1]新疆农业大学机电工程学院,乌鲁木齐830052 [2]北京市农林科学院智能装备技术研究中心,北京100097 [3]农业农村部华北都市农业重点实验室,北京100096

出　　处：《农业机械学报》2024年第3期352-361,共10页Transactions of the Chinese Society for Agricultural Machinery

基　　金：国家食用菌产业技术体系项目(CARS-20);北京市食用菌创新团队项目(BAIC03-2023)。

摘　　要：当前工厂化食用菌生产菇房空调控制方法存在节能效率低、室内温度波动大等问题,提出了一种基于卷积神经网络(Convolutional neural network, CNN)、门控循环单元神经网络(Gated recurrent unit neural network, GRU)与注意力机制(Attention)的菇房空调节能控制方法。该方法以CNN-GRU-Attention组合神经网络为预测模型,结合预测误差补偿和预测模型数据集动态更新机制,实现对菇房室内温度精准预测;建立以空调控制量为状态量的目标函数,分别利用熵权法、主观法明确目标函数权重系数,运用带精英策略的快速非支配排序遗传算法(Non-dominated sorting genetic algorithmⅡ, NSGA-Ⅱ)求解出空调在控制时域内最优控制序列,集成滚动优化和反馈机制,实现菇房环境的精准及节能控制。试验结果表明,提出的CNN-GRU-Attention菇房室内温度预测模型,以历史30 min数据预测未来10 min室内温度效果最好,选取的典型日内预测最大均方根误差为0.122℃、最小决定系数为0.807、最大平均绝对百分比误差为0.611%;菇房空调模型预测控制方法对天气波动具有较好的抗干扰能力。与阈值开关法和PID法相比,在空调节能方面,能耗分别减少21%和14%;在控制温度精度方面,RMSE可分别降低72%、46%。At present,there are some problems such as low energy saving efficiency and large indoor temperature fluctuation in the control methods of mushroom air conditioning in factory production.An energy saving control method based on convolutional neural network(CNN),gated recurrent unit neural network(GRU)and self-attention mechanism was proposed.The CNN-GRU-Attention combined neural network was used as the prediction model,and the prediction error compensation and the dynamic updating mechanism of the prediction model data set were combined to achieve accurate prediction of indoor temperature in mushroom houses.The control quantity of air conditioning was established as the objective function of state quantity,and the weight coefficient of the objective function was defined by entropy weight method and subjective method,respectively.The optimal control sequence of air conditioning in the control time domain was solved by non-dominated sorting genetic algorithmⅡ(NSGA-Ⅱ),and the rolling optimization and feedback mechanism were integrated to realize the accurate and energy-saving control of the greenhouse environment.The experimental results showed that the CNN-GRU-Attention indoor temperature prediction model proposed in mushroom house showed that the previous 30min data had the best effect in predicting the indoor temperature in the future 10min.On a typical intra-day the maximum root mean square error of prediction accuracy was 0.122℃,the minimum coefficient of determination was 0.807,and the maximum mean absolute percentage error was 0.611%.The model predictive control method of mushroom air conditioning had a good anti-interference ability in weather fluctuation.Compared with threshold switching method and PID method,the energy consumption of air conditioning can be saved by 21%and 14%,respectively.In terms of temperature control accuracy,the root mean square error was decreased by 72%and 46%,respectively.

关 键 词：菇房 预测模型 模型预测控制 多目标优化 神经网络 

分 类 号：TP273.1[自动化与计算机技术—检测技术与自动化装置]