基于CNN-LSTM混合神经网络模型的NOx排放预测

为了充分挖掘电站锅炉NO_(x)排放数据中时序性特征联系,提高NO_(x)排放预测精度,提出一种基于卷积神经网络(CNN)和长短期记忆网络(LSTM)的NO_(x)排放预测方法。以某300 MW电站锅炉历史数据为样本,采用K-means聚类方法对NO_(x)排放训练样本集进行分组,再基于CNN网络的卷积层和池化层提取NO_(x)排放变量的高维映射关系,构造高维时序特征向量,将抽象化的特征集输入到LSTM网络,通过训练LSTM网络参数建立基于CNN-LSTM的NO_(x)排放预测模型。通过某电站锅炉实际数据验证,所提预测模型对训练和测试样本的平均相对百分比误差分别为1.76%和3.85%,远低于其他模型。结果表明所提模型在预测精度和泛化能力方面具有显著优势。

NOx emission forecasting based on CNN-LSTM hybrid neural network

In order to fully exploit the relationship between temporal features in NOx emission data and improve the accuracy of NOx emission forecasting results, this paper proposes a NOx emission forecasting method based on a hybrid neural network model of convolutional neural network (CNN) and long short-term memory network (LSTM). Taking the historical data of a 300MW coal-fired boiler as a sample, the k-means clustering method is used to group NOx emission sample sets. Then the high-dimensional mapping relationship of NOx emission variables is extracted based on the convolutional layer and pooling layer of the CNN network to construct a high-dimensional time series feature vector, which is input the LSTM network. A NOx emission prediction model is established based on CNN-LSTM by training LSTM network parameters. Through the actual data verification of coal-fired boiler, the Mean relative percentage error of the proposed prediction model for training and testing samples are 1.76% and 3.85%, respectively, which are much lower than other models. The results show that the proposed NOx emission prediction model has significant advantages in terms of prediction accuracy and generalization ability.