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A novel fractional order grey Euler model and its application in clean energy prediction

Author

Listed:
  • Yang, Zhongsen
  • Wang, Yong
  • Fan, Neng
  • Wen, Shixiong
  • Kuang, Wenyu
  • Yang, Mou
  • Sapnken, Flavian Emmanuel
  • Narayanan, Govindasami
  • Li, Hong-Li

Abstract

The global imperative for sustainable energy solutions has intensified due to escalating energy demands and heightened environmental regulations, making clean energy adoption critical for strategic policymaking. This study introduces an advanced fractional grey Euler model (FEGEM(p,1)) incorporating a novel fractional-order accumulated generating operation with exponential kernel functionality (r-EAGO) to forecast clean energy production trends in hydroelectricity, nuclear power, and natural gas. The proposed r-EAGO mechanism demonstrates superior capability in extracting implicit patterns from historical data through its sophisticated accumulation process. The fractional-order modelling framework offers enhanced flexibility through its adjustable differential order, particularly effective in addressing the inherent non-linearity and complexity of clean energy systems. To ensure optimal performance, the model hyperparameters are optimised using a differential evolution algorithm. The results of Monte-Carlo simulation and probability density analysis reveal good robustness of the model. Comprehensive validation of the seven forecasting models revealed that the FEGEM(p,1) model is extremely accurate, achieving a mean absolute percentage error (MAPE) of 2.508 %, 3.489 %, and 2.343 % for the three energy cases, respectively. Projections of the next five years indicate distinct growth trajectories: Nuclear power generation and natural gas production are forecasted to maintain compound annual growth rates of 4.65 % and 5.46 % respectively through 2027, while hydropower exhibits cyclical growth patterns influenced by hydrological variations, ultimately reaching 1,429 billion kWh by the terminal forecast year. Finally, these findings yield strategic recommendations for optimising energy development framework.

Suggested Citation

  • Yang, Zhongsen & Wang, Yong & Fan, Neng & Wen, Shixiong & Kuang, Wenyu & Yang, Mou & Sapnken, Flavian Emmanuel & Narayanan, Govindasami & Li, Hong-Li, 2025. "A novel fractional order grey Euler model and its application in clean energy prediction," Energy, Elsevier, vol. 322(C).
  • Handle: RePEc:eee:energy:v:322:y:2025:i:c:s0360544225012514
    DOI: 10.1016/j.energy.2025.135609
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    1. Wang, Yong & Yang, Zhongsen & Wang, Li & Ma, Xin & Wu, Wenqing & Ye, Lingling & Zhou, Ying & Luo, Yongxian, 2022. "Forecasting China's energy production and consumption based on a novel structural adaptive Caputo fractional grey prediction model," Energy, Elsevier, vol. 259(C).
    2. He, Xinbo & Wang, Yong & Zhang, Yuyang & Ma, Xin & Wu, Wenqing & Zhang, Lei, 2022. "A novel structure adaptive new information priority discrete grey prediction model and its application in renewable energy generation forecasting," Applied Energy, Elsevier, vol. 325(C).
    3. 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).
    4. Ding, Song & Li, Ruojin & Wu, Shu & Zhou, Weijie, 2021. "Application of a novel structure-adaptative grey model with adjustable time power item for nuclear energy consumption forecasting," Applied Energy, Elsevier, vol. 298(C).
    5. Wang, Yong & Yang, Zhongsen & Ye, Lingling & Wang, Li & Zhou, Ying & Luo, Yongxian, 2023. "A novel self-adaptive fractional grey Euler model with dynamic accumulation order and its application in energy production prediction of China," Energy, Elsevier, vol. 265(C).
    6. Yin, Chen & Mao, Shuhua, 2023. "Fractional multivariate grey Bernoulli model combined with improved grey wolf algorithm: Application in short-term power load forecasting," Energy, Elsevier, vol. 269(C).
    7. Wang, Yong & Sun, Lang & Yang, Rui & He, Wenao & Tang, Yanbing & Zhang, Zejia & Wang, Yunhui & Sapnken, Flavian Emmanuel, 2023. "A novel structure adaptive fractional derivative grey model and its application in energy consumption prediction," Energy, Elsevier, vol. 282(C).
    8. 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.
    9. Wang, Yong & Yang, Rui & Zhang, Juan & Sun, Lang & Xiao, Wenlian & Saxena, Akash, 2024. "A novel structure adaptive discrete grey Bernoulli prediction model and its applications in energy consumption and production," Energy, Elsevier, vol. 291(C).
    10. Chen, Yan & Lifeng, Wu & Lianyi, Liu & Kai, Zhang, 2020. "Fractional Hausdorff grey model and its properties," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    11. Wenqing Wu & Xin Ma & Bo Zeng & Yuanyuan Zhang & Wanpeng Li, 2021. "Forecasting short-term solar energy generation in Asia Pacific using a nonlinear grey Bernoulli model with time power term," Energy & Environment, , vol. 32(5), pages 759-783, August.
    12. Yang, Hufang & Jiang, Ping & Wang, Ying & Li, Hongmin, 2022. "A fuzzy intelligent forecasting system based on combined fuzzification strategy and improved optimization algorithm for renewable energy power generation," Applied Energy, Elsevier, vol. 325(C).
    13. Peng-Yu Chen & Hong-Ming Yu, 2014. "Foundation Settlement Prediction Based on a Novel NGM Model," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-8, March.
    14. Zhang, Yandi, 2025. "Enhancing solar irradiance prediction for sustainable energy solutions employing a hybrid machine learning model; improving hydrogen production through Photoelectrochemical device," Applied Energy, Elsevier, vol. 382(C).
    15. 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.
    16. Zhong, Weiyi & Zhai, Dengshuai & Xu, Wenran & Gong, Wenwen & Yan, Chao & Zhang, Yang & Qi, Lianyong, 2024. "Accurate and efficient daily carbon emission forecasting based on improved ARIMA," Applied Energy, Elsevier, vol. 376(PA).
    Full references (including those not matched with items on IDEAS)

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