Abstract: Temperature is one of the main limiting factors of crop growth in facility production. It is of great guiding significance to predict air temperature in the greenhouse in advance for managing and controlling the environment in the greenhouse accurately. Long ShortTerm Memory network (LSTM) based on Grey Wolf Optimization (GWO) model was proposed to predict air temperature in the greenhouse in this paper. This model used GWO to adjust and optimize the parameters of LSTM, which could avoid manual adjustment of parameters and improve the efficiency of model parameter adjustment. The experimental greenhouse was located in Jiangsu Academy of Agricultural Sciences. The data of environment and control device operation status were collected from September 23rd, 2020 to December 21st, 2020 during the experiment. The results showed that when the predicted time step was 30 min, the root mean square error, mean absolute error, mean absolute percentage error and determination coefficient of GMO-LSTM prediction were 0.677 6、0411 4、0.168 7 and 0.960 4, respectively. In the prediction time step of 60 min, the prediction accuracy of GMO-LSTM was higher than that of standard LSTM and Back Propagation Artificial Neural Network (BP-ANN). In summary, GWO-LSTM model proposed in this paper could accurately predict the future temperature change in the greenhouse, which could also provide the effective data support for developing intelligent control strategy of the environment in the greenhouse.

Key words: greenhouse, temperature, time series, Long ShortTerm Memory network, Grey Wolf Optimization algorithm

摘要： 温度是设施生产中作物生长的主要制约因素之一，提前预测温室温度对精准调控温室环境具有重要的指导意义。因此提出一种基于灰狼优化算法的长短期记忆网络模型预测温室温度，该模型利用灰狼优化算法(Grey Wolf Optimizer，GWO)对长短期记忆网络(Long ShortTerm Memory，LSTM)模型参数进行调整优化。以江苏省农业科学院阳光板温室2020年9月23日—12月21日期间的试验数据对该方法进行验证。结果显示：在预测时间步长30 min时，GWO-LSTM 的预测均方根误差、平均绝对误差、平均绝对百分比误差和决定系数分别为0.677 6、0.411 4、0.168 7和0.960 4。在预测时间步长60 min内，GWO-LSTM模型预测精度均高于标准LSTM和反向传播人工神经网络(Back Propagation Artificial Neural Network，BP-ANN)。说明所提出的GWO-LSTM模型能够准确地预测未来温室内温度变化，可为制定温室环境智能调控策略提供有效的数据支撑。

关键词: 温室, 温度, 时间序列, 长短期记忆网络, 灰狼优化算法