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Updated: Jun 9, 2025

Measurements of Waves in a Wind-wave Tank Under Steady and Time-varying Wind Forcing
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Short-Term Wind Power Interval Forecasting Based on Hybrid Modal Decomposition and Improved Optimization.

Jixuan Wang1, Yifan Tang1, Zengfu Xi1

  • 1College of Water Conservancy and Hydropower, Handan 056038, China.

Anais Da Academia Brasileira De Ciencias
|October 23, 2024
PubMed
Summary
This summary is machine-generated.

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This study introduces a hybrid approach for accurate wind power interval prediction, improving grid stability and economic dispatch. The method combines advanced decomposition techniques with optimized forecasting for reliable energy management.

Area of Science:

  • Renewable Energy Systems
  • Power System Operations
  • Computational Intelligence

Background:

  • Accurate wind power forecasting is crucial for power system stability and economic dispatch.
  • Existing methods often struggle with the inherent volatility and non-stationarity of wind power data.
  • The need for reliable interval predictions to quantify forecast uncertainty is paramount.

Purpose of the Study:

  • To develop a hybrid approach for enhanced wind power interval prediction accuracy.
  • To improve the economic dispatch and peak frequency regulation of power systems.
  • To provide reliable uncertainty quantification for wind power forecasts.

Main Methods:

  • Decomposition of wind power sequences using Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) and Variational Mode Decomposition.

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  • Complexity assessment of decomposed components using Fuzzy Entropy (FE).
  • Hybrid forecasting utilizing component-wise predictions, optimized hyperparameters via an improved sparrow search algorithm (ISSA), and interval construction with kernel density estimation (KDE).

    • Main Results:

    • Achieved root mean square errors (RMSE) of 2.8458 MW and 1.8605 MW for deterministic predictions.
    • Demonstrated high uncertainty coverage rates of 95.83% and 97.92% at a 95% confidence level.
    • The hybrid approach effectively captures wind power dynamics and uncertainty.

    Conclusions:

    • The proposed hybrid method significantly enhances wind power interval prediction accuracy.
    • This approach contributes to more stable and economically efficient power system operations.
    • The integration of advanced decomposition, complexity analysis, and optimization provides a robust framework for wind power forecasting.