Journal of South China University of Technology(Natural Science Edition) ›› 2025, Vol. 53 ›› Issue (11): 1-.doi: 10.12141/j.issn.1000-565X.250086

• Computer Science & Technology •    

CO2 Emission Measurement and Forecasting for Power Plants Based on Multi-Source Data and Machine Learning

SUN Zunqiang1  TIAN Yichun2  SU Nan2  ZHENG Chenghang3  ZHANG Zhen4  YANG Hongmin4  GAO Xiang3   

  1. 1. State Power Environmental Protection Research Instituter, Nanjing 210031, Jiangsu, China；

    2. National Institute of Clean and Low-carbon Energy, Beijing 102200, China;

    3. Zhejiang University, Hangzhou 310027, Zhejiang, China;

    4. School of Energy and Mechanical Engineering, Nanjing Normal University, Nanjing 210042, Jiangsu, China

  • Online:2025-11-25 Published:2025-07-01

Abstract:

Accurate CO2 emission measurement and dynamic forecasting are crucial for achieving China's "dual carbon" goals (carbon peak and carbon neutrality). This study integrates the emission factor method, CO2-CEMS, and machine learning technologies to propose a CO2 emission measurement and forecasting method based on multi-source data fusion, aimed at providing an efficient and accurate carbon emission monitoring tool for coal-fired power plants. By comparing the CO2 emission calculation results of different methods, the study evaluates the performance of machine learning algorithms, such as Adaboost, and develops a dynamic forecasting model based on multi-source data. Experimental results show that in Units 1 and 3 of a power plant in Hebei, China, the relative deviations of CO2 emission calculations between the emission factor method and CO2-CEMS were 1.63% and -1.27%, respectively, demonstrating good cross-validation between the two methods. Multiple machine learning models (such as XGBoost, LightGBM, etc.) were compared against the AdaBoost model. In addition to conventional evaluation metrics such as R² and MAPE, a novel selection criterion, namely the mean deviation (xc) obtained by applying trained models to other units, was proposed to assess the generalization capability of machine learning algorithms for further model screening. The results revealed that AdaBoost exhibited superior performance in prediction accuracy and stability, along with higher generalization capability and robustness. The dynamic CO2 emission forecasting using the optimized Adaboost algorithm achieved R² values greater than 0.99 on both the training and testing sets, with a mean absolute percentage error (MAPE) below 2%, indicating high prediction accuracy, stability, generalization ability, and robustness. The proposed multi-source data fusion method not only effectively overcomes the limitations of traditional methods in dynamic scenarios but also enables precise hourly CO2 emission forecasting based on real-time data.

Key words: CO2, carbon measurement, carbon forecasting, emission factor method, machine learning