Journal of South China University of Technology(Natural Science) >

2025 , Vol. 53 >Issue 11: 52 - 61

DOI: https://doi.org/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 Zunqiang ,
  • TIAN Yichun ,
  • SU Nan ,
  • ZHENG Chenghang ,
  • ZHANG Zhen ,
  • YANG Hongmin ,
  • GAO Xiang
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  • 1.State Power Environmental Protection Research Institute，Nanjing 210031，Jiangsu，China
    2.National Institute of Clean and Low-Carbon Energy，Beijing 102200，China
    3.College of Energy Engineering，Zhejiang University，Hangzhou 310027，Zhejiang，China
    4.School of Energy and Mechanical Engineering，Nanjing Normal University，Nanjing 210042，Jiangsu，China
孙尊强（1982—），男，博士生，高级工程师，主要从事电力环境保护设计研究。E-mail： sunzunqiang@126.com

Received date: 2025-03-31

  Online published: 2025-06-30

Supported by

the National Key Research & Development Program of China(2022YFC3701504)

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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, for the purpose of providing an efficient and accurate carbon emission monitoring tool for coal-fired power plants. In the investigation, by comparing the CO2 emission calculation results obtained by different methods, the performances of different machine learning algorithms are evaluated, and a dynamic forecasting model based on multi-source data is developed. Experimental results in Units 1 and 3 of a power plant of Guoneng Group Co., Ltd. in Hebei, China show that the relative deviations of CO2 emission calculations between the emission factor method and CO2-CEMS for the two units are 1.63% and -1.27%, respectively, meaning that the two methods are of good cross-validation. By comparing the performance of various machine learning models (such as XGBoost, LightGBM, and AdaBoost), beyond the two conventional evaluation metrics, namely the determination coefficient (R²) and the mean absolute percentage error (MAPE), a new selection criterion, namely the mean deviation (xc), is proposed by applying trained models to other units. Then, xc is used to assess the generalization capability of machine learning algorithms for further model screening. The results reveal that AdaBoost exhibits superior performance in prediction accuracy and stability, along with higher generalization capability and robustness. The dynamic CO2 emission forecasting using the optimized AdaBoost algorithm achieves R² values greater than 0.99 on both the training and the testing sets, with a MAPE below 2%, which indicates that the algorithm is of 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： carbon dioxide; carbon measurement; carbon forecasting; emission factor method; machine learning

Cite this article

SUN Zunqiang , TIAN Yichun , SU Nan , ZHENG Chenghang , ZHANG Zhen , YANG Hongmin , GAO Xiang . CO2 Emission Measurement and Forecasting for Power Plants Based on Multi-Source Data and Machine Learning[J]. Journal of South China University of Technology(Natural Science), 2025 , 53(11) : 52 -61 . DOI: 10.12141/j.issn.1000-565X.250086

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