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

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.
• 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.