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Branch error reduction criterion-based signal recursive decomposition and its application to wind power generation forecasting

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  • Fen Xiao
  • Siyu Yang
  • Xiao Li
  • Junhong Ni

Abstract

Due to the ability of sidestepping mode aliasing and endpoint effects, variational mode decomposition (VMD) is usually used as the forecasting module of a hybrid model in time-series forecasting. However, the forecast accuracy of the hybrid model is sensitive to the manually set mode number of VMD; neither underdecomposition (the mode number is too small) nor over-decomposition (the mode number is too large) improves forecasting accuracy. To address this issue, a branch error reduction (BER) criterion is proposed in this study that is based on which a mode number adaptive VMD-based recursive decomposition method is used. This decomposition method is combined with commonly used single forecasting models and applied to the wind power generation forecasting task. Experimental results validate the effectiveness of the proposed combination.

Suggested Citation

  • Fen Xiao & Siyu Yang & Xiao Li & Junhong Ni, 2024. "Branch error reduction criterion-based signal recursive decomposition and its application to wind power generation forecasting," PLOS ONE, Public Library of Science, vol. 19(3), pages 1-20, March.
    • Handle: RePEc:plo:pone00:0299955
    DOI: 10.1371/journal.pone.0299955
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    1. Wu, Chunying & Wang, Jianzhou & Hao, Yan, 2022. "Deterministic and uncertainty crude oil price forecasting based on outlier detection and modified multi-objective optimization algorithm," Resources Policy, Elsevier, vol. 77(C).
    2. Quande Qin & Huangda He & Li Li & Ling-Yun He, 2020. "A Novel Decomposition-Ensemble Based Carbon Price Forecasting Model Integrated with Local Polynomial Prediction," Computational Economics, Springer;Society for Computational Economics, vol. 55(4), pages 1249-1273, April.
    3. Min Yi & Wei Xie & Li Mo, 2021. "Short-Term Electricity Price Forecasting Based on BP Neural Network Optimized by SAPSO," Energies, MDPI, vol. 14(20), pages 1-17, October.
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    1. Nan Zhang & Yawen Zhai & Yan Li & Jiayu Zhou & Mingming Zhai & Chi Tang & Kangning Xie, 2024. "Kalman filtering to reduce measurement noise of sample entropy: An electroencephalographic study," PLOS ONE, Public Library of Science, vol. 19(7), pages 1-21, July.

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