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China's coal consumption forecasting using adaptive differential evolution algorithm and support vector machine

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  • Mengshu, Shi
  • Yuansheng, Huang
  • Xiaofeng, Xu
  • Dunnan, Liu

Abstract

Coal consumption forecasting is the premise of energy supply structure reform and the basic work of energy planning under the background of climate change. Based on the econometrics which determines the influencing factors, this paper proposes a combined algorithm using Self-adaptive differential evolution (SaDE) algorithm and support vector (SVM) optimization algorithm for coal consumption forecasting. The optimization algorithm reduces the selection problem of SVM oversized hyperplane parameters, improves its global optimization ability, and further improves the prediction accuracy of SVM. The results of China's coal consumption forecasting show that the improved SaDE-SVM algorithm has good adaptability, robustness, faster converging speed and higher prediction accuracy for the prediction of less data samples and multiple influencing factors suitable for relevant medium and long term forecasts. The forecast shows that China's coal consumption is expected to be between 3.92 billion tons and 4.14 billion tons by 2020. By 2030, China's coal consumption will be between 2.195 billion tons and 3.699 billion tons.

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  • Mengshu, Shi & Yuansheng, Huang & Xiaofeng, Xu & Dunnan, Liu, 2021. "China's coal consumption forecasting using adaptive differential evolution algorithm and support vector machine," Resources Policy, Elsevier, vol. 74(C).
    • Handle: RePEc:eee:jrpoli:v:74:y:2021:i:c:s0301420721002981
    DOI: 10.1016/j.resourpol.2021.102287
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    1. Koot, Martijn & Wijnhoven, Fons, 2021. "Usage impact on data center electricity needs: A system dynamic forecasting model," Applied Energy, Elsevier, vol. 291(C).
    2. Wang, Ce & Li, Bing-Bing & Liang, Qiao-Mei & Wang, Jin-Cheng, 2018. "Has China’s coal consumption already peaked? A demand-side analysis based on hybrid prediction models," Energy, Elsevier, vol. 162(C), pages 272-281.
    3. Yanbin Li & Zhen Li, 2019. "Forecasting of Coal Demand in China Based on Support Vector Machine Optimized by the Improved Gravitational Search Algorithm," Energies, MDPI, vol. 12(12), pages 1-20, June.
    4. Teng, Meixuan & Burke, Paul J. & Liao, Hua, 2019. "The demand for coal among China's rural households: Estimates of price and income elasticities," Energy Economics, Elsevier, vol. 80(C), pages 928-936.
    5. Zhang Xu & Darong Huang & Tang Min & Yunhui Ou, 2020. "A Fault Diagnosis Method of Rolling Bearing Integrated with Cooperative Energy Feature Extraction and Improved Least-Squares Support Vector Machine," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-13, December.
    6. David A. Mascio & Frank J. Fabozzi & J. Kenton Zumwalt, 2021. "Market timing using combined forecasts and machine learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(1), pages 1-16, January.
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