A novel Bayesian ensembling model for wind power forecasting.

Area of Science:

• Renewable Energy Systems
• Machine Learning Applications
• Time Series Forecasting

Background:

• Wind power generation is inherently variable and unpredictable, posing challenges for grid integration.
• Accurate wind power prediction is crucial for grid stability and efficient energy management.
• Ensemble learning offers a promising approach to enhance forecasting accuracy and reliability.

Purpose of the Study:

• To develop a novel ensemble probabilistic forecasting framework for wind power.
• To improve the precision and robustness of wind power predictions.
• To quantify the uncertainty associated with wind power forecasts.

Main Methods:

• Utilized a modified randomized maximum a posteriori (MAP) sampling technique to train independent echo state network (ESN) models.
• Employed a generalized mixture (GM) function for dynamic weighting and ensembling of base model predictions.
• Implemented and evaluated the proposed framework on four diverse wind power datasets.

Main Results:

• The proposed ensemble framework demonstrated superior forecasting performance compared to benchmark models.
• The model successfully achieved dynamic ensemble probabilistic prediction.
• Validation across multiple datasets confirmed the method's effectiveness and generalizability.

Conclusions:

• The novel ensemble probabilistic forecasting framework offers significant advantages for wind power prediction.
• The integration of modified randomized MAP, ESN, and GM function leads to enhanced accuracy and robustness.
• This approach effectively addresses the challenges posed by wind power's inherent variability.