A method for measuring carbon emissions from power plants using a CNN-LSTM-Attention model with Bayesian optimization
Jiacheng Chen, Zheng Li, Wenyan Che, Li Liu, Hui Huang, Jun Liu, Xing Chang, Penghua Qiu
Abstract
Accurate measurement of CO 2 emissions from coal-fired power plants is crucial for achieving China's “carbon neutrality” goal; however, traditional CO 2 emission measurement methods have several limitations, and emerging prediction models rarely utilize real-time monitoring data from power plants at the micro level . To address these challenges, this study presents a novel hybrid convolutional neural network with long short-term memory (CNN-LSTM)-Attention model, leveraging real-time monitoring data to enhance the accuracy of CO 2 emission predictions. The proposed model employs a hybrid architecture that leverages the strength of CNN, LSTM, and Attention mechanism. To address the challenges of multiple hyperparameters and difficult selection, the Bayesian optimization algorithm is used to optimize the hyperparameters ensuring optimal performance . Using historical operational data from a 660 MW generator set in Hubei province, the proposed model was evaluated and validated horizontally by using three evaluation metrics. Additionally, the vertical evaluation and validation of the proposed model were done using the operational data from other months. The results indicate that the proposed model achieved the highest performance across every evaluation index for predicting CO 2 emissions from coalfired power plants, and the prediction accuracy of the CO 2 emission in the future months is also guaranteed.