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Carbon Emission Forecasting Study Based on Influence Factor Mining and Mini-Batch Stochastic Gradient Optimization

Author

Listed:
  • Wei Yang

    (Big Data Center of State Grid Corporation of China, Beijing 100052, China)

  • Qiheng Yuan

    (Big Data Center of State Grid Corporation of China, Beijing 100052, China)

  • Yongli Wang

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Fei Zheng

    (Beijing China-Power Information Technology Co., Ltd., Beijing 100089, China)

  • Xin Shi

    (Big Data Center of State Grid Corporation of China, Beijing 100052, China)

  • Yi Li

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

Abstract

With the increasing prominence of the global carbon emission problem, the accurate prediction of carbon emissions has become an increasingly urgent need. Existing carbon emission prediction methods have the problems of slow calculation speed, inaccurate prediction, and insufficient deep mining of influencing factors when dealing with large-scale data. In this study, a comprehensive carbon emission prediction method is proposed. Firstly, multiple influencing factors including economic factors and demographic factors are considered, and a pathway analysis method is introduced to mine the long-term relationship between these factors and carbon emissions. Then, indirect influence terms are added to the multiple regression equation, and the variable is used to represent the indirect influence relationship. Finally, this study proposes the PCA-PA-MBGD method, which applies the results of principal component analysis to the pathway analysis. By reducing the data dimensions and extracting the main influencing factors, and optimizing the carbon emission prediction model by using a mini-batch stochastic gradient descent algorithm, the results show that this method can process a large amount of data quickly and efficiently, and realize an accurate prediction of carbon emissions. This provides strong support for solving the carbon emission problem and offers new ideas and methods for future related research.

Suggested Citation

  • Wei Yang & Qiheng Yuan & Yongli Wang & Fei Zheng & Xin Shi & Yi Li, 2023. "Carbon Emission Forecasting Study Based on Influence Factor Mining and Mini-Batch Stochastic Gradient Optimization," Energies, MDPI, vol. 17(1), pages 1-18, December.
    • Handle: RePEc:gam:jeners:v:17:y:2023:i:1:p:188-:d:1309806
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    References listed on IDEAS

    as
    1. Peng Fang, 2023. "Short-term carbon emission prediction method of green building based on IPAT model," International Journal of Global Energy Issues, Inderscience Enterprises Ltd, vol. 45(1), pages 1-13.
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