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A novel self-adaptive fractional grey Euler model with dynamic accumulation order and its application in energy production prediction of China

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Listed:
  • Wang, Yong
  • Yang, Zhongsen
  • Ye, Lingling
  • Wang, Li
  • Zhou, Ying
  • Luo, Yongxian

Abstract

In the fractional grey model, the new information priority principle has always been pursued by researchers. However, it does not mean that new information is the most important, because in reality, there are always some special moments, and the information they represent will profoundly affect the future development. That is, the most important data may be in the middle of the sequence, not the latest data. Therefore, this paper proposes a periodic variable order accumulated generating operation, which can switch the priority of new and old information with time. In terms of model structure, this paper presents a new variable coefficient whitening equation with self-adaptive structure. That is, a new fractional order grey Euler model with dynamic accumulation order (DOFGM(1,1)) is proposed in this paper. The new model combines the adjacent sequence operator, therefore the newly generated sequence is less random. By adjusting the model parameters, the structure of the model can be changed, so that the model has the features of selecting the adaptive structure. By comparison, the differential evolution algorithm is selected to optimize the hyperparameters of the model. In particular, the robustness of the model is validated by introducing perturbations to the raw data to imitate errors generated during data collection. Finally, three actual cases of China's hydropower generation, China's natural gas production and China's total primary energy production are predicted; the prediction result shows that the new model has higher prediction accuracy than the other six models. The results indicate that the proposed model benefits from its adaptive structure and produces reliable predictions. According to these prediction results, relevant suggestions on the development of China's energy are provided to decision makers.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:265:y:2023:i:c:s0360544222032704
    DOI: 10.1016/j.energy.2022.126384
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    References listed on IDEAS

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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).
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    Cited by:

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    2. Wang, Yong & He, Xinbo & Zhou, Ying & Luo, Yongxian & Tang, Yanbing & Narayanan, Govindasami, 2024. "A novel structure adaptive grey seasonal model with data reorganization and its application in solar photovoltaic power generation prediction," Energy, Elsevier, vol. 302(C).
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    4. Ding, Yuanping & Dang, Yaoguo, 2023. "Forecasting renewable energy generation with a novel flexible nonlinear multivariable discrete grey prediction model," Energy, Elsevier, vol. 277(C).
    5. Yang, Zhongsen & Wang, Yong & Zhou, Ying & Wang, Li & Ye, Lingling & Luo, Yongxian, 2023. "Forecasting China's electricity generation using a novel structural adaptive discrete grey Bernoulli model," Energy, Elsevier, vol. 278(C).

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