1002-6819
2024
40
18
194
201
10.11975/j.issn.1002-6819.202404199
article
基于LSTM-AT的温室空气温度预测模型构建
Predicting greenhouse air temperature using LSTM-AT
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 can be required for the greenhouse air temperature, 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. Therefore, the prediction accuracy of the LSTM-AT model was higher than that of rest four models. 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. 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.
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
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
nygcxb/article/abstract/20241820