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Updated: Sep 9, 2025

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Probabilistic machine learning-based forecasting of wind speed uncertainty using adaptive kernel density estimation.

Rami Al-Hajj1

  • 1College of Engineering and Technology, American University of the Middle East, Kuwait.

Mathematical Biosciences and Engineering : MBE
|September 3, 2025
PubMed
Summary
This summary is machine-generated.

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Accurate short-term wind speed forecasting is vital for renewable energy. This study introduces a hybrid Support Vector Regression with Adaptive Kernel Density Estimation (SVR-AKDE) model for precise prediction intervals, improving wind energy reliability.

Area of Science:

  • Renewable Energy Systems
  • Machine Learning Applications
  • Statistical Forecasting

Background:

  • Short-term wind speed forecasting is critical for efficient wind energy integration.
  • Conventional point predictions lack accuracy in capturing wind speed uncertainty.
  • Quantifying forecast uncertainty is essential for reliable wind energy operations.

Purpose of the Study:

  • To develop a hybrid forecasting methodology for short-term wind speed prediction intervals.
  • To quantify forecast uncertainty using Support Vector Regression (SVR) and Adaptive Kernel Density Estimation (AKDE).
  • To evaluate the proposed SVR-AKDE model against conventional methods for improved uncertainty estimation.

Main Methods:

  • A hybrid model combining Support Vector Regression (SVR) with Adaptive Kernel Density Estimation (AKDE) was developed.
Keywords:
AKDESVRadaptive kernel density estimatorprediction intervalsprobabilistic energy forecastingrenewable energysupport vector regressorswind speed forecasting

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  • Adaptive KDE was utilized to adjust bandwidth based on local forecast error distribution for precise uncertainty quantification.
  • The SVR-AKDE model was assessed for short-term horizons (10, 30, 60, 120 minutes).

    • Main Results:

    • The SVR-AKDE model demonstrated superior performance in estimating wind speed prediction intervals.
    • The proposed method consistently provided enhanced prediction interval coverage probability (PICP) and narrower prediction interval normalized average width (PINAW).
    • Simulation findings confirmed the efficacy of SVR-AKDE over traditional KDE-based interval estimation.

    Conclusions:

    • The SVR-AKDE hybrid model offers a robust solution for short-term wind speed forecasting with quantifiable uncertainty.
    • This approach enhances the reliability and operational control of wind energy installations.
    • Accurate uncertainty quantification is key to maximizing the potential of wind power generation.