摘要
构建精确的温室空气温度预测模型是采用模型预测控制等控制算法实现温室空气温度精准控制的前提条件。长短记忆神经网络(long short-term memory,LSTM)以处理时间序列数据方面的优势而广泛应用于温室空气温度预测,然而其面对长时间序列数据存在由于数据遗忘而导致温室空气温度预测精度降低的问题。为解决以上问题,该研究将LSTM模型与注意力机制(attention mechanism,AT)结合构建LSTM-AT模型,根据LSTM模型隐藏层输出状态重要性程度为隐藏层输出分配权重,以提高温室空气温度长时间预测精度。该研究在不同预测时长(12、24和48 h)和不同天气状况两种情况下将LSTM-AT模型与递归神经网络、门控循环单元、双向长短记忆网络、LSTM模型进行对比。结果表明,LSTM-AT模型空气温度预测值与测量值变化趋势较为一致,模型计算值与空气温度测量值的决定系数最小为0.95,均方根误差最大为1.34℃,平均绝对误差最大为10.51%;LSTM-AT模型、LSTM模型、门控循环单元、递归神经网络、双向长短记忆网络5种模型温室空气温度预测均方根误差平均值分别为0.89、1.42、1.89、2.10、1.51℃,平均绝对百分比误差平均值分别为4.26%、8.96%、13.57%、17.70%、10.67%。由此可知,相较于其他4种模型,该研究提出的LSTM-AT模型具有更高的预测精度,能够精确预测温室空气温度。
An accurate prediction model of greenhouse air temperature can be required for the greenhouse environment control using control algorithm,such as model predictive control.Long short-term memory neural networks(LSTM)have been widely used to predict time series data,such as air temperature.However,the prediction accuracy of LSTM can be reduced for the long time series data,due to data forgetting.In this study,the LSTM model was combined with the attention mechanism to construct the LSTM-AT model.The query vector,key vector,and value vector were calculated,according to attention mechanism and output states of LSTM’s hidden layer.The similarity between the query and key vector was calculated to obtain the similarity score.Softmax function was used to obtain attention distribution for the normalization processing.The larger the attention value was,the higher the relevance of input information to the task objective was.The dot product operation was carried out with the normalized weight and value vector to obtain the output of the attention mechanism.The local information integration and data dimension transformation were carried out through the full connection layer.Finally,the output data was obtained in the output layer of the LSTM-AT model.The weights were assigned to the output states of LSTM’s hidden layer,according to the degree of importance.The forgetting of long time series data was effectively solved to improve the prediction accuracy of indoor air temperature.The prediction performances were verified and compared on the LSTM-AT,LSTM,recurrent neural network(RNN),gated recurrent unit(GRU),and bi-directional long short-term memory(BiLSTM)model in the different prediction horizons(12,24 and 48 h).The results showed that the prediction accuracy of the five models shared a decreasing trend with the increase in prediction time.The maximum and minimum RMSE for the LSTM-AT model were 1.34 and 0.59℃,respectively.The maximum and minimum RMSE for the rest four models were 3.37 and 0.66℃,respectively.The maximum and minimum MAPE for the LSTM-AT model were 8.14%and 2.48%,respectively.The maximum and minimum MAPE for the rest four models were 38.7%and 2.90%,respectively.The average RMSE for LSTM-AT,LSTM,GRU,RNN,and BiLSTM were 0.89,1.42,1.89,2.10,and 1.51℃,respectively.The average MAPE for LSTM-AT,LSTM,GRU,RNN,and BiLSTM were 4.26%,8.96%,13.57%,17.70%,and 10.67%,respectively.The sort data of the prediction model was ranked in descending order of the LSTM-AT,LSTM,BiLSTM,GRU,and RNN.Therefore,the prediction accuracy of the LSTM-AT model was higher than that of rest four models.The prediction performances of the LSTM-AT and LSTM model were compared under different weather conditions(sunny,cloudy,and rainy),in order to further explore the universality of the LSTM-AT model.The minimum and maximum RMSE for LSTM-AT were 0.26 and 0.70℃,respectively.The minimum and maximum RMSE for LSTM were 0.68 and 1.57℃,respectively.The minimum and maximum MAPE for LSTM-AT were 1.61%and 10.51%,respectively.The minimum and maximum MAPE for LSTM were 4.27%and 25.07%,respectively.The prediction accuracy of the LSTM-AT model was higher than LSTM in all weather conditions.The LSTM-AT model has a higher prediction accuracy to accurately predict the indoor air temperature.
作者
张观山
丁小明
何芬
尹义蕾
李天华
任吉傲
周俊毅
齐飞
ZHANG Guanshan;DING Xiaoming;HE Fen;YIN Yilei;LI Tianhua;REN Ji'ao;ZHOU Junyi;QI Fei(Academy of Agricultural Planning and Engineering,Ministry of Agriculture and Rural Affairs,Beijing 100125,China;College of Mechanical and Electronic Engineering,Shandong Agricultural University,Tai’an 271018,China;Key Laboratory of Farm Building in Structure and Intelligent Construction,Ministry of Agriculture and Rural Affairs,Beijing 100125,China)
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2024年第18期194-201,共8页
Transactions of the Chinese Society of Agricultural Engineering
基金
农业农村部规划设计研究院自主研发计划项目(SP202101)
国家自然科学基金项目(32201657)
山东省自然科学基金项目(ZR2021QF091)
山东省重点研发计划重大科技创新工程项目(2022CXGC020708)
山东省重点研发计划乡村振兴科技创新提振行动计划项目(2023TZXD065)。
关键词
温室
空气温度
长短记忆神经网络
注意力机制
预测模型
greenhouse
air temperature
long short-term memory neural network
attention mechanism
prediction model
