Adaptive machine learning for forecasting in wind energy: A dynamic, multi-algorithmic approach for short and...

Area of Science:

• Renewable Energy Systems
• Machine Learning Applications
• Predictive Analytics

Background:

• Accurate wind energy forecasting is crucial for grid stability and efficient renewable energy integration.
• Existing forecasting models often struggle with varying accuracy for short-term and long-term predictions.
• The integration of multiple machine learning algorithms can potentially enhance forecasting performance.

Purpose of the Study:

• To develop and validate a dynamic hybrid model for accurate wind energy forecasting.
• To improve both short-term (15-min intervals) and long-term predictive accuracy.
• To adaptively select the most effective forecasting technique based on historical performance.

Main Methods:

• Assimilation of time-series wind energy generation data.
• Integration of Artificial Neural Network (ANN), Support Vector Machine (SVM), and K-Nearest Neighbors (K-NN) algorithms.
• Development of a dynamic switching mechanism for adaptive algorithm selection.
• Comparative performance evaluation using a 2 MW grid-connected wind turbine dataset.

Main Results:

• The dynamic hybrid model demonstrated superior forecasting accuracy compared to individual algorithms and existing literature.
• Achieved a Normalized Mean Absolute Error (NMAE) of 5.54%, outperforming other models (NMAE 5.65%-9.22%).
• The model showed significant improvements in both short-term and long-term wind energy predictions.

Conclusions:

• The proposed dynamic hybrid model offers a robust and versatile solution for wind energy forecasting.
• Its adaptive nature makes it highly valuable for grid operators and wind farm management.
• The model's success suggests potential applicability in other predictive analytics domains.