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Research on China's energy supply and demand using an improved Grey-Markov chain model based on wavelet transform

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  • Wei, Sun
  • Yanfeng, Xu

Abstract

Energy is an essential foundation for the economic growth and social development of a country. Since the reform and opening up, the economy in China has been in a state of rapid development and the paradox of the energy supply-demand has become increasingly prominent. An improved Grey-Markov chain model based on the wavelet transform is presented in this paper which takes various energy forms into account. The model uses the discrete wavelet transform for denoising, substitutes the extended grey model to the traditional one and introduces the fuzzy theory and metabolic principle into the Markov chain. Then an empirical example of China's energy production and consumption data during the period of 1990–2014 were selected as the research objects. Comparing with other methods, the proposed model was proved feasible and valid, and the energy production and consumption situation from 2015 to 2020 was predicted. Besides, the trend of energy supply and demand gap was analyzed as well as the energy structure which utilized the Shannon Wiener index. The results show that in the foreseeable future, the energy supply and demand gap in China will narrow and the energy structure tends to be diversified. Finally some opinions and suggestions are put forward.

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  • Wei, Sun & Yanfeng, Xu, 2017. "Research on China's energy supply and demand using an improved Grey-Markov chain model based on wavelet transform," Energy, Elsevier, vol. 118(C), pages 969-984.
    • Handle: RePEc:eee:energy:v:118:y:2017:i:c:p:969-984
    DOI: 10.1016/j.energy.2016.10.120
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    References listed on IDEAS

    as
    1. Khan, Muhammad Arshad, 2015. "Modelling and forecasting the demand for natural gas in Pakistan," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 1145-1159.
    2. Kumar, Ujjwal & Jain, V.K., 2010. "Time series models (Grey-Markov, Grey Model with rolling mechanism and singular spectrum analysis) to forecast energy consumption in India," Energy, Elsevier, vol. 35(4), pages 1709-1716.
    3. Perwez, Usama & Sohail, Ahmed & Hassan, Syed Fahad & Zia, Usman, 2015. "The long-term forecast of Pakistan's electricity supply and demand: An application of long range energy alternatives planning," Energy, Elsevier, vol. 93(P2), pages 2423-2435.
    4. Melikoglu, Mehmet, 2013. "Vision 2023: Forecasting Turkey's natural gas demand between 2013 and 2030," Renewable and Sustainable Energy Reviews, Elsevier, vol. 22(C), pages 393-400.
    5. Giglmayr, Sebastian & Brent, Alan C. & Gauché, Paul & Fechner, Hubert, 2015. "Utility-scale PV power and energy supply outlook for South Africa in 2015," Renewable Energy, Elsevier, vol. 83(C), pages 779-785.
    6. Quiggin, Daniel & Buswell, Richard, 2016. "The implications of heat electrification on national electrical supply-demand balance under published 2050 energy scenarios," Energy, Elsevier, vol. 98(C), pages 253-270.
    7. Sun, Xu & Sun, Wangshu & Wang, Jianzhou & Zhang, Yixin & Gao, Yining, 2016. "Using a Grey–Markov model optimized by Cuckoo search algorithm to forecast the annual foreign tourist arrivals to China," Tourism Management, Elsevier, vol. 52(C), pages 369-379.
    8. Miara, Ariel & Tarr, Craig & Spellman, Rachel & Vörösmarty, Charles J. & Macknick, Jordan E., 2014. "The power of efficiency: Optimizing environmental and social benefits through demand-side-management," Energy, Elsevier, vol. 76(C), pages 502-512.
    9. Huang, Yophy & Bor, Yunchang Jeffrey & Peng, Chieh-Yu, 2011. "The long-term forecast of Taiwan’s energy supply and demand: LEAP model application," Energy Policy, Elsevier, vol. 39(11), pages 6790-6803.
    10. Li, Bing-Bing & Liang, Qiao-Mei & Wang, Jin-Cheng, 2015. "A comparative study on prediction methods for China's medium- and long-term coal demand," Energy, Elsevier, vol. 93(P2), pages 1671-1683.
    11. Khanna, Nina Zheng & Guo, Jin & Zheng, Xinye, 2016. "Effects of demand side management on Chinese household electricity consumption: Empirical findings from Chinese household survey," Energy Policy, Elsevier, vol. 95(C), pages 113-125.
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    Cited by:

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    2. Yang Zhang & Zhenghui Fu & Yulei Xie & Qing Hu & Zheng Li & Huaicheng Guo, 2020. "A Comprehensive Forecasting–Optimization Analysis Framework for Environmental-Oriented Power System Management—A Case Study of Harbin City, China," Sustainability, MDPI, vol. 12(10), pages 1-26, May.
    3. Xu, Ning & Ding, Song & Gong, Yande & Bai, Ju, 2019. "Forecasting Chinese greenhouse gas emissions from energy consumption using a novel grey rolling model," Energy, Elsevier, vol. 175(C), pages 218-227.
    4. Liu, Zhiyuan & Yu, Hang & Liu, Rui, 2019. "A novel energy supply and demand matching model in park integrated energy system," Energy, Elsevier, vol. 176(C), pages 1007-1019.
    5. Tan, Qinliang & Ding, Yihong & Ye, Qi & Mei, Shufan & Zhang, Yimei & Wei, Yongmei, 2019. "Optimization and evaluation of a dispatch model for an integrated wind-photovoltaic-thermal power system based on dynamic carbon emissions trading," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    6. Jinghan Chen & Wen Zhou & Hongtao Yang & Zhuofei Wu, 2021. "“Grouping” or “Ride One’s Coattails”?—How Developing Countries along the Belt and Road Satisfy Themselves," Energies, MDPI, vol. 14(12), pages 1-22, June.
    7. Jia, Zong-qian & Zhou, Zhi-fang & Zhang, Hong-jie & Li, Bo & Zhang, You-xian, 2020. "Forecast of coal consumption in Gansu Province based on Grey-Markov chain model," Energy, Elsevier, vol. 199(C).
    8. R. Rajesh, 2022. "A novel advanced grey incidence analysis for investigating the level of resilience in supply chains," Annals of Operations Research, Springer, vol. 308(1), pages 441-490, January.
    9. Zhou, Cheng & Chen, Xiyang, 2019. "Predicting energy consumption: A multiple decomposition-ensemble approach," Energy, Elsevier, vol. 189(C).

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