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Predictive analysis on electric-power supply and demand in China

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  • Huang, Min
  • He, Yong
  • Cen, Haiyan

Abstract

In order to analyze the electric-power demand and supply in China efficiently, this paper presents a Grey–Markov forecasting model to forecast the electric-power demand in China. This method takes into account the general trend series and random fluctuations about original time-series data. It has the merits of both simplicity of application and high forecasting precision. This paper was based on historical data of the electric-power requirement from 1985 to 2001 in China, and forecasted and analyzed the electric-power supply and demand in China by the Grey–Markov forecasting model. The forecasting precision of Grey-Markov forecasting model from 2002 to 2004 is 99.42%, 98.05% and 97.56% respectively, and in GM(1,1) Grey forecasting model, it is 98.53%, 94.02% and 88.48%, respectively. It shows that the Grey–Markov forecasting models has higher precision than GM(1,1) Grey forecasting model. The forecast values from 2002 to 2013 were as follows: 16106.7, 18541.3, 20575.7, 23940.5, 24498.0, 26785.1, 27977.2, 29032.2, 31247.5, 33428.8, 35865.4, and 38399.3TWh. The results provide scientific basis for the planned development of the electric-power supply in China.

Suggested Citation

  • Huang, Min & He, Yong & Cen, Haiyan, 2007. "Predictive analysis on electric-power supply and demand in China," Renewable Energy, Elsevier, vol. 32(7), pages 1165-1174.
    • Handle: RePEc:eee:renene:v:32:y:2007:i:7:p:1165-1174
    DOI: 10.1016/j.renene.2006.04.005
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    Citations

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    Cited by:

    1. Wang, Jianzhou & Zhu, Wenjin & Zhang, Wenyu & Sun, Donghuai, 2009. "A trend fixed on firstly and seasonal adjustment model combined with the [epsilon]-SVR for short-term forecasting of electricity demand," Energy Policy, Elsevier, vol. 37(11), pages 4901-4909, November.
    2. Zhu, Suling & Wang, Jianzhou & Zhao, Weigang & Wang, Jujie, 2011. "A seasonal hybrid procedure for electricity demand forecasting in China," Applied Energy, Elsevier, vol. 88(11), pages 3807-3815.
    3. 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.
    4. Yu, Shiwei & Wei, Yi-Ming & Wang, Ke, 2012. "A PSO–GA optimal model to estimate primary energy demand of China," Energy Policy, Elsevier, vol. 42(C), pages 329-340.
    5. 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.
    6. Hamzacebi, Coskun & Es, Huseyin Avni, 2014. "Forecasting the annual electricity consumption of Turkey using an optimized grey model," Energy, Elsevier, vol. 70(C), pages 165-171.
    7. Shi, Kaifang & Yu, Bailang & Huang, Chang & Wu, Jianping & Sun, Xiufeng, 2018. "Exploring spatiotemporal patterns of electric power consumption in countries along the Belt and Road," Energy, Elsevier, vol. 150(C), pages 847-859.
    8. Yu, Shi-wei & Zhu, Ke-jun, 2012. "A hybrid procedure for energy demand forecasting in China," Energy, Elsevier, vol. 37(1), pages 396-404.
    9. Wang, Li'ao & Hu, Gang & Gong, Xun & Bao, Liang, 2009. "Emission reductions potential for energy from municipal solid waste incineration in Chongqing," Renewable Energy, Elsevier, vol. 34(9), pages 2074-2079.
    10. Hu, Ting & Huang, Xin, 2019. "A novel locally adaptive method for modeling the spatiotemporal dynamics of global electric power consumption based on DMSP-OLS nighttime stable light data," Applied Energy, Elsevier, vol. 240(C), pages 778-792.
    11. Wang, Ju-Jie & Wang, Jian-Zhou & Zhang, Zhe-George & Guo, Shu-Po, 2012. "Stock index forecasting based on a hybrid model," Omega, Elsevier, vol. 40(6), pages 758-766.
    12. Wang, Jianzhou & Zhu, Suling & Zhang, Wenyu & Lu, Haiyan, 2010. "Combined modeling for electric load forecasting with adaptive particle swarm optimization," Energy, Elsevier, vol. 35(4), pages 1671-1678.
    13. Shi, Kaifang & Yang, Qingyuan & Fang, Guangliang & Yu, Bailang & Chen, Zuoqi & Yang, Chengshu & Wu, Jianping, 2019. "Evaluating spatiotemporal patterns of urban electricity consumption within different spatial boundaries: A case study of Chongqing, China," Energy, Elsevier, vol. 167(C), pages 641-653.
    14. Zhao, Weigang & Wang, Jianzhou & Lu, Haiyan, 2014. "Combining forecasts of electricity consumption in China with time-varying weights updated by a high-order Markov chain model," Omega, Elsevier, vol. 45(C), pages 80-91.
    15. Gao, Xin & Ye, Yunxia & Su, Wenxin & Chen, Linyan, 2023. "Assessing the comprehensive importance of power grid nodes based on DEA," International Journal of Critical Infrastructure Protection, Elsevier, vol. 42(C).
    16. Shi, Kaifang & Chen, Yun & Yu, Bailang & Xu, Tingbao & Yang, Chengshu & Li, Linyi & Huang, Chang & Chen, Zuoqi & Liu, Rui & Wu, Jianping, 2016. "Detecting spatiotemporal dynamics of global electric power consumption using DMSP-OLS nighttime stable light data," Applied Energy, Elsevier, vol. 184(C), pages 450-463.
    17. Tiantian Zhang & Ken’ichi Matsumoto & Kei Nakagawa, 2021. "The Relative Importance of Determinants of the Solar Photovoltaic Industry in China: Analyses by the Diamond Model and the Analytic Hierarchy Process," Energies, MDPI, vol. 14(20), pages 1-20, October.

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