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Forecasting energy consumption using a new GM–ARMA model based on HP filter: The case of Guangdong Province of China

Author

Listed:
  • Xu, Weijun
  • Gu, Ren
  • Liu, Youzhu
  • Dai, Yongwu

Abstract

Guangdong's energy reduction requirements developed by the State Council of China under the 12th Five-year Plan reflect a reality: the restriction brought by energy consumption on economic development in Guangdong is tougher. To obtain a detailed understanding of the future amount of Guangdong's energy consumption in the coming years, this paper establishes a new model with improved GM–ARMA based on HP Filter to forecast the final energy consumption. Compared with traditional statistical approaches, the case study of Guangdong indicates that the improved GM–ARMA model has excellent accuracy and higher level of reliability. Moreover, based on this model, this paper predicts the energy consumption under different future economic scenarios and forecasts the future changes in the structure of the final energy consumption in Guangdong from 2013 to 2016 to discuss Guangdong's possibility of achieving the reduction goal. Finally, this paper finds that the issue of energy saving and emission reduction is very serious in the next few years.

Suggested Citation

  • Xu, Weijun & Gu, Ren & Liu, Youzhu & Dai, Yongwu, 2015. "Forecasting energy consumption using a new GM–ARMA model based on HP filter: The case of Guangdong Province of China," Economic Modelling, Elsevier, vol. 45(C), pages 127-135.
  • Handle: RePEc:eee:ecmode:v:45:y:2015:i:c:p:127-135
    DOI: 10.1016/j.econmod.2014.11.011
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    References listed on IDEAS

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    Cited by:

    1. repec:eee:eneeco:v:70:y:2018:i:c:p:244-257 is not listed on IDEAS
    2. Zeng, Bo & Li, Chuan, 2016. "Forecasting the natural gas demand in China using a self-adapting intelligent grey model," Energy, Elsevier, vol. 112(C), pages 810-825.
    3. Adom, Philip Kofi & Amakye, Kwaku & Barnor, Charles & Quartey, George & Bekoe, William, 2016. "Shift in demand elasticities, road energy forecast and the persistence profile of shocks," Economic Modelling, Elsevier, vol. 55(C), pages 189-206.
    4. repec:eee:rensus:v:88:y:2018:i:c:p:297-325 is not listed on IDEAS
    5. repec:gam:jeners:v:11:y:2018:i:4:p:825-:d:139356 is not listed on IDEAS
    6. repec:eee:rensus:v:75:y:2017:i:c:p:123-136 is not listed on IDEAS
    7. Yuan, Chaoqing & Liu, Sifeng & Fang, Zhigeng, 2016. "Comparison of China's primary energy consumption forecasting by using ARIMA (the autoregressive integrated moving average) model and GM(1,1) model," Energy, Elsevier, vol. 100(C), pages 384-390.

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