Temporal Convolutional Network for Gas Concentration Prediction Based on Weighted Loss and Channel Coupling Attention

Gas sensors form a critical foundation for gas concentration prediction (GCP) tasks, providing the necessary technological support for the transmission and processing of industrial gas data. However, existing deep learning-based GCP models often overlook the coupling information between features and have limited generalizability. Consequently, these models exhibit low prediction accuracy when dealing with data characterized by numerous anomalies and significant fluctuations. Therefore, this article proposes a gas prediction model based on the weighted loss channel-coupled attentional time convolutional network (WLCCTCN), which aims to capture the coupled information among channels and suppress the abundance of abnormal data with large fluctuations in different environments. The WLCCTCN primarily consists of three structures: 1) causal dilated convolution structure; 2) channel-coupled attention structure; and 3) a weighted loss function. Firstly, the design of the strip matrix of the channel-coupled attention module (CCAM) is utilized to capture the complex dependencies between multiple features fully. Secondly, the weighted loss function (EALoss) is employed to suppress abnormal features and fluctuations in gas data. Finally, experimental results with various gas datasets demonstrate that the proposed model captures coupling information between channels in gas concentration data and effectively suppresses data abnormalities and fluctuations. This proves the superior predictive capability of the proposed model compared to existing state-of-the-art methods.