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Forecasting China's renewable energy consumption using a novel dynamic fractional-order discrete grey multi-power model

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  • Xia, Lin
  • Ren, Youyang
  • Wang, Yuhong
  • Pan, Yangyang
  • Fu, Yiyang

Abstract

Accurately predicting renewable energy consumption is crucial for sustainable social and economic development, especially in China during its energy transition. This research introduces a novel dynamic fractional-order discrete grey multi-power model (DFDGMM(1,1,N)) to enable accurate forecasting of renewable energy consumption in China. The proposed method introduces a fractional-order accumulation operator and three power exponents that not only ensure the priority of new information, but also accurately capture the nonlinear traits of system data. It also incorporates a dynamic time delay function to account for the time lag between energy and economic development, enhancing the model's flexibility. Additionally, the study combines the whale optimization algorithm and the double-error idea to optimal parameter search. The proposed model is versatile and can be simplified into 14 other grey models. The case study demonstrates the model's impressive predictive accuracy, with a fitting error of 4.02 % and a test error of 0.89 %. The model is then employed to forecast renewable energy consumption in China, predicting a rapid annual growth rate of 17.25 % from 2022 to 2030. Overall, this article successfully constructs a dynamic prediction model in theory and scientifically provides valuable data support for the nation's energy development planning in practice.

Suggested Citation

  • Xia, Lin & Ren, Youyang & Wang, Yuhong & Pan, Yangyang & Fu, Yiyang, 2024. "Forecasting China's renewable energy consumption using a novel dynamic fractional-order discrete grey multi-power model," Renewable Energy, Elsevier, vol. 233(C).
    • Handle: RePEc:eee:renene:v:233:y:2024:i:c:s0960148124011935
    DOI: 10.1016/j.renene.2024.121125
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    References listed on IDEAS

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