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Forecasting Carbon Emissions Related to Energy Consumption in Beijing-Tianjin-Hebei Region Based on Grey Prediction Theory and Extreme Learning Machine Optimized by Support Vector Machine Algorithm

Author

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  • Menglu Li

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China
    Beijing Key Laboratory of New Energy and Low-Carbon Development, North China Electric Power University, Beijing 102206, China)

  • Wei Wang

    (School of Humanities and Social Sciences, North China Electric Power University, Beijing 102206, China)

  • Gejirifu De

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China
    Beijing Key Laboratory of New Energy and Low-Carbon Development, North China Electric Power University, Beijing 102206, China)

  • Xionghua Ji

    (School of Economics and Management, Yan’an University, Yan’an 716000, China)

  • Zhongfu Tan

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China
    Beijing Key Laboratory of New Energy and Low-Carbon Development, North China Electric Power University, Beijing 102206, China
    School of Economics and Management, Yan’an University, Yan’an 716000, China)

Abstract

Carbon emissions and environmental protection issues have brought pressure from the international community during Chinese economic development. Recently, Chinese Government announced that carbon emissions per unit of GDP would fall by 60–65% compared with 2005 and non-fossil fuel energy would account for 20% of primary energy consumption by 2030. The Beijing-Tianjin-Hebei region is an important regional energy consumption center in China, and its energy structure is typically coal-based which is similar to the whole country. Therefore, forecasting energy consumption related carbon emissions is of great significance to emissions reduction and upgrading of energy supply in the Beijing-Tianjin-Hebei region. Thus, this study thoroughly analyzed the main energy sources of carbon emissions including coal, petrol, natural gas, and coal power in this region. Secondly, the kernel function of the support vector machine was applied to the extreme learning machine algorithm to optimize the connection weight matrix between the original hidden layer and the output layer. Thirdly, the grey prediction theory was used to predict major energy consumption in the region from 2017 to 2030. Then, the energy consumption and carbon emissions data for 2000–2016 were used as the training and test sets for the SVM-ELM (Support Vector Machine-Extreme Learning Machine) model. The result of SVM-ELM model was compared with the forecasting results of SVM (Support Vector Machine Algorithm) and ELM (Extreme Learning Machine) algorithm. The accuracy of SVM-ELM was shown to be higher. Finally, we used forecasting output of GM (Grey Prediction Theory) (1, 1) as the input of the SVM-ELM model to predict carbon emissions in the region from 2017 to 2030. The results showed that the proportion of energy consumption seriously affects the amount of carbon emissions. We found that the energy consumption of electricity and natural gas will reach 45% by 2030 and carbon emissions in the region can be controlled below 96.9 million tons. Therefore, accelerating the upgradation of industrial structure will be the key task for the government in controlling the amount of carbon emissions in the next step.

Suggested Citation

  • Menglu Li & Wei Wang & Gejirifu De & Xionghua Ji & Zhongfu Tan, 2018. "Forecasting Carbon Emissions Related to Energy Consumption in Beijing-Tianjin-Hebei Region Based on Grey Prediction Theory and Extreme Learning Machine Optimized by Support Vector Machine Algorithm," Energies, MDPI, vol. 11(9), pages 1-15, September.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:9:p:2475-:d:170423
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    References listed on IDEAS

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    5. Peng Jiang & Jun Dong & Hui Huang, 2019. "Forecasting China’s Renewable Energy Terminal Power Consumption Based on Empirical Mode Decomposition and an Improved Extreme Learning Machine Optimized by a Bacterial Foraging Algorithm," Energies, MDPI, vol. 12(7), pages 1-24, April.
    6. Wenting Zhao & Juanjuan Zhao & Xilong Yao & Zhixin Jin & Pan Wang, 2019. "A Novel Adaptive Intelligent Ensemble Model for Forecasting Primary Energy Demand," Energies, MDPI, vol. 12(7), pages 1-28, April.
    7. Qifan Guan, 2023. "Decomposing and Decoupling the Energy-Related Carbon Emissions in the Beijing–Tianjin–Hebei Region Using the Extended LMDI and Tapio Index Model," Sustainability, MDPI, vol. 15(12), pages 1-17, June.
    8. Huang, Jiaqing & Wang, Linlin & Siddik, Abu Bakkar & Abdul-Samad, Zulkiflee & Bhardwaj, Arpit & Singh, Bharat, 2023. "Forecasting GHG emissions for environmental protection with energy consumption reduction from renewable sources: A sustainable environmental system," Ecological Modelling, Elsevier, vol. 475(C).
    9. Shuxia Yang & Xianguo Zhu & Shengjiang Peng, 2020. "Prospect Prediction of Terminal Clean Power Consumption in China via LSSVM Algorithm Based on Improved Evolutionary Game Theory," Energies, MDPI, vol. 13(8), pages 1-17, April.
    10. Wei Sun & Yufei Hou & Lanjiang Guo, 2018. "Analyzing and Forecasting Energy Consumption in China’s Manufacturing Industry and Its Subindustries," Sustainability, MDPI, vol. 11(1), pages 1-26, December.

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