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Forecasting China’s Coal Power Installed Capacity: A Comparison of MGM, ARIMA, GM-ARIMA, and NMGM Models

Author

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  • Shuyu Li

    (School of Economic and Management, China University of Petroleum (East China), Qingdao 266580, China)

  • Xue Yang

    (School of Economic and Management, China University of Petroleum (East China), Qingdao 266580, China)

  • Rongrong Li

    (School of Economic and Management, China University of Petroleum (East China), Qingdao 266580, China
    School of Management & Economics, Beijing Institute of Technology, Haidian District, Beijing 100081, China)

Abstract

Construction of new coal-fired power plants in China has posed a huge challenge to energy sustainability. Forecasting the installed capacity more accurately can serve to develop better energy sustainability strategy. A comparison between linear and non-linear forecasting models can more comprehensively describe the characteristics of the prediction data and provide multi-angle analysis of the prediction results. In this paper, we develop four time-series forecasting techniques—metabolism grey model (MGM), autoregressive integrated moving average (ARIMA), grey model (GM)-ARIAM, and nonlinear metabolism grey model (NMGM)—for better forecasting of coal-fired power installed capacity. The average relative errors between the simulation and actual data of the MGM, GM-ARIMA, ARIMA, and NMGM model are 3.37%, 2.13%, 3.71% and 2.36% respectively, which indicate those four models can produce highly accurate results. The forecasting results show the average annual growth rate of China’s coal-fired power installed capacity in the next ten years (2017–2016) will be 5.26% a year, which is slower than the average annual growth rate (8.20% a year) for 2007–2016. However, the average annual new added installed capacity for 2017–2026 will be 74 gigawatts, which is higher than the average annual added installed capacity (56 gigawatts) for 2007–2016.

Suggested Citation

  • Shuyu Li & Xue Yang & Rongrong Li, 2018. "Forecasting China’s Coal Power Installed Capacity: A Comparison of MGM, ARIMA, GM-ARIMA, and NMGM Models," Sustainability, MDPI, vol. 10(2), pages 1-15, February.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:2:p:506-:d:131779
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    References listed on IDEAS

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    Cited by:

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    2. Jindamas Sutthichaimethee & Kuskana Kubaha, 2018. "Forecasting Energy-Related Carbon Dioxide Emissions in Thailand’s Construction Sector by Enriching the LS-ARIMAXi-ECM Model," Sustainability, MDPI, vol. 10(10), pages 1-19, October.
    3. Wang, Qiang & Li, Shuyu & Li, Rongrong, 2018. "China's dependency on foreign oil will exceed 80% by 2030: Developing a novel NMGM-ARIMA to forecast China's foreign oil dependence from two dimensions," Energy, Elsevier, vol. 163(C), pages 151-167.
    4. Feng, Qianqian & Sun, Xiaolei & Hao, Jun & Li, Jianping, 2021. "Predictability dynamics of multifactor-influenced installed capacity: A perspective of country clustering," Energy, Elsevier, vol. 214(C).
    5. Minglu Ma & Min Su & Shuyu Li & Feng Jiang & Rongrong Li, 2018. "Predicting Coal Consumption in South Africa Based on Linear (Metabolic Grey Model), Nonlinear (Non-Linear Grey Model), and Combined (Metabolic Grey Model-Autoregressive Integrated Moving Average Model," Sustainability, MDPI, vol. 10(7), pages 1-15, July.
    6. Feng Jiang & Xue Yang & Shuyu Li, 2018. "Comparison of Forecasting India’s Energy Demand Using an MGM, ARIMA Model, MGM-ARIMA Model, and BP Neural Network Model," Sustainability, MDPI, vol. 10(7), pages 1-17, June.
    7. Luo, Xilin & Duan, Huiming & He, Leiyuhang, 2020. "A Novel Riccati Equation Grey Model And Its Application In Forecasting Clean Energy," Energy, Elsevier, vol. 205(C).

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