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A novel interval forecasting system for uncertainty modeling based on multi-input multi-output theory: A case study on modern wind stations

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  • Liu, Tongxiang
  • Zhao, Qiujun
  • Wang, Jianzhou
  • Gao, Yuyang

Abstract

With the growing demand for a clean energy source, wind power is drawing increasing attention. However, its intermittence and fluctuation set strict restrictions on its development and applications. Although a vast amount of research has been conducted on this subject, studies have failed to characterize the uncertainties of the growing intervals and have focus only on point prediction. Therefore, this paper proposes an interval prediction system that can effectively avoid the drawbacks of point forecasting. The system is composed of five units: a preprocessing unit, a feature selection unit, an optimization unit, a forecasting unit, and a result evaluation unit. The preprocessing unit, along with the feature selection unit, is applied to obtain the ideal input data. Then, the forecasting unit, whose key parameters are updated by the optimization unit, is used for interval prediction. The experimental results obtained from various evaluation metrics show that the accuracy of the developed system exceeds that of benchmark methods, and also confirm the possibility of applying the proposed method in the effective utilization of wind energy.

Suggested Citation

  • Liu, Tongxiang & Zhao, Qiujun & Wang, Jianzhou & Gao, Yuyang, 2021. "A novel interval forecasting system for uncertainty modeling based on multi-input multi-output theory: A case study on modern wind stations," Renewable Energy, Elsevier, vol. 163(C), pages 88-104.
    • Handle: RePEc:eee:renene:v:163:y:2021:i:c:p:88-104
    DOI: 10.1016/j.renene.2020.08.139
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    References listed on IDEAS

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    Cited by:

    1. Jiang, Sufan & Wu, Chuanshen & Gao, Shan & Pan, Guangsheng & Liu, Yu & Zhao, Xin & Wang, Sicheng, 2022. "Robust frequency risk-constrained unit commitment model for AC-DC system considering wind uncertainty," Renewable Energy, Elsevier, vol. 195(C), pages 395-406.
    2. Wang, Jianzhou & Gao, Jialu & Wei, Danxiang, 2022. "Electric load prediction based on a novel combined interval forecasting system," Applied Energy, Elsevier, vol. 322(C).
    3. Zhu, Qiannan & Jiang, Feng & Li, Chaoshun, 2023. "Time-varying interval prediction and decision-making for short-term wind power using convolutional gated recurrent unit and multi-objective elephant clan optimization," Energy, Elsevier, vol. 271(C).

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