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Self-adaptive discrete grey model based on a novel fractional order reverse accumulation sequence and its application in forecasting clean energy power generation in China

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  • Wang, Yong
  • Chi, Pei
  • Nie, Rui
  • Ma, Xin
  • Wu, Wenqing
  • Guo, Binghong

Abstract

With the increasing power consumption in China and the urgent demand for environmental protection, promoting the development of clean energy power generation industry is the only way to optimize the energy power generation structure. It is very important to effectively predict the development trend of China's clean energy power generation system with complex, changeable and limited data. To address this issue, this paper defines a novel fractional self-adaptive reverse accumulation sequence, and combines discrete modeling techniques and time power terms to propose a novel fractional self-adaptive reverse accumulation with time power terms. The parameter estimation and time response formula of the new model are derived. The matrix perturbation theory is used to prove that the new model satisfies the new information priority principle. The Grey Wolf Optimizer is used to optimize the self-adaptive parameter r and non-negative constant α. Finally, the prediction model is constructed for the power generation capacity of five representative types of clean energy in China: biomass, wind, nuclear, natural gas and hydro power, the prediction result shows that the new model has higher prediction accuracy and data applicability than the other five grey models. According to these prediction results, relevant suggestions on the development of China's clean energy are provided to decision makers.

Suggested Citation

  • Wang, Yong & Chi, Pei & Nie, Rui & Ma, Xin & Wu, Wenqing & Guo, Binghong, 2022. "Self-adaptive discrete grey model based on a novel fractional order reverse accumulation sequence and its application in forecasting clean energy power generation in China," Energy, Elsevier, vol. 253(C).
    • Handle: RePEc:eee:energy:v:253:y:2022:i:c:s0360544222009963
    DOI: 10.1016/j.energy.2022.124093
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    References listed on IDEAS

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    1. Wu, Wenqing & Ma, Xin & Zeng, Bo & Wang, Yong & Cai, Wei, 2018. "Application of the novel fractional grey model FAGMO(1,1,k) to predict China's nuclear energy consumption," Energy, Elsevier, vol. 165(PB), pages 223-234.
    2. Pali, Bahadur Singh & Vadhera, Shelly, 2018. "A novel pumped hydro-energy storage scheme with wind energy for power generation at constant voltage in rural areas," Renewable Energy, Elsevier, vol. 127(C), pages 802-810.
    3. Zhu, Xiaoyue & Dang, Yaoguo & Ding, Song, 2020. "Using a self-adaptive grey fractional weighted model to forecast Jiangsu’s electricity consumption in China," Energy, Elsevier, vol. 190(C).
    4. Wu, Wenqing & Ma, Xin & Zeng, Bo & Wang, Yong & Cai, Wei, 2019. "Forecasting short-term renewable energy consumption of China using a novel fractional nonlinear grey Bernoulli model," Renewable Energy, Elsevier, vol. 140(C), pages 70-87.
    5. Ma, Xin & Mei, Xie & Wu, Wenqing & Wu, Xinxing & Zeng, Bo, 2019. "A novel fractional time delayed grey model with Grey Wolf Optimizer and its applications in forecasting the natural gas and coal consumption in Chongqing China," Energy, Elsevier, vol. 178(C), pages 487-507.
    6. Wang, Meng & Wang, Wei & Wu, Lifeng, 2022. "Application of a new grey multivariate forecasting model in the forecasting of energy consumption in 7 regions of China," Energy, Elsevier, vol. 243(C).
    7. Wen-Yeau Chang, 2013. "Short-Term Wind Power Forecasting Using the Enhanced Particle Swarm Optimization Based Hybrid Method," Energies, MDPI, vol. 6(9), pages 1-18, September.
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