A real-time correction model for carbon emission measurement data and carbon emission factors in coal-fired power plants based on data fusion Academic Article uri icon

abstract

  • Abstract Carbon emissions from coal-fired power plants contribute to approximately half of the total national carbon emissions, making accurate measurement of these emissions essential for achieving the “double carbon”. Currently, the most widely used methods for measuring carbon emissions are the material balance method, the flue gas measurement method, and the carbon emission factor method. However, fluctuations in coal quality and inaccuracies in measurement equipment result in significant variability in the granularity and accuracy of carbon measurements. Thus, this paper proposed a real-time correction model for carbon emission measurement based on data fusion, in order to achieve a low-carbon transition of power plants. The differences between the calculation results of the two different methods were quantified and the reasons for the differences were analyzed by using on-site measured data. Then, combining the advantages of the flue gas measurement method and the carbon emission factor method, the Kalman filter was used for data fusion, and the carbon emission factor was corrected in real time using this as a benchmark. The results show that data fusion can significantly improve the data quality of carbon emissions from coal-fired power plants and reduce the random errors. The difference in carbon emissions of the fused values under similar working conditions can be reduced by 41.35%, and the standard deviation of the fused difference values is reduced by 47.02%, which verifies the effectiveness of the method.

publication date

  • 2025

start page

  • 012033

volume

  • 3001

issue

  • 1