A method for measuring carbon emissions from power plants using a CNN-LSTM-Attention model with Bayesian optimization

Accurate measurement of CO2 emissions from coal-fired power plants is crucial for achieving China's “carbon neutrality” goal; however, traditional CO2 emission measurement methods have several limitations, and emerging prediction models rarely utilize real-time monitoring data from power plants...

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Bibliographic Details
Main Authors:	Jiacheng Chen, Li Zheng, Wenyan Che, Li Liu, Hui Huang, Jun Liu, Chang Xing, Penghua Qiu
Format:	Article
Language:	English
Published:	Elsevier 2024-11-01
Series:	Case Studies in Thermal Engineering
Subjects:	Coal-fired power plant Carbon measurement in power plant Long short-term memory neural network Convolutional neural network Bayesian optimization
Online Access:	http://www.sciencedirect.com/science/article/pii/S2214157X24013650
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A method for measuring carbon emissions from power plants using a CNN-LSTM-Attention model with Bayesian optimization

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