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Short-term wind power forecasting with an intermittency-trait-driven methodology

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  • Ma, Yixiang
  • Yu, Lean
  • Zhang, Guoxing

Abstract

To improve the forecasting accuracy of short-term wind power, an intermittency-trait-driven methodology is proposed in this paper. In the proposed methodology, four main steps, i.e., intermittency-trait-driven data decomposition, intermittency-trait-driven mode reconstruction, intermittency-trait-driven component prediction and intermittency-trait-driven ensemble output, are involved. In particular, the selected distribution function driven by intermittency trait analysis is used to improve the decomposition model, so as to solve the end effect issue in the rolling decomposition. For illustration and verification, the wind power data with 15-min intervals is introduced as the sample data. Compared with the benchmark models, the root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) of the proposed model is reduced by 22.357%, 26.457% and 23.828% on average, respectively. The results indicate that the proposed intermittency-trait-driven methodology outperforms all benchmark models in multi-step-ahead forecasting, which can provide valuable insights to effective wind power prediction.

Suggested Citation

  • Ma, Yixiang & Yu, Lean & Zhang, Guoxing, 2022. "Short-term wind power forecasting with an intermittency-trait-driven methodology," Renewable Energy, Elsevier, vol. 198(C), pages 872-883.
    • Handle: RePEc:eee:renene:v:198:y:2022:i:c:p:872-883
    DOI: 10.1016/j.renene.2022.08.079
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