Ranking-oriented machine learning framework for probabilistic wind power forecasting with temporal reliability...

Area of Science:

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

Background:

• Accurate wind power forecasting is crucial for grid stability and energy market efficiency.
• Traditional methods often overlook output ordering and temporal consistency, impacting critical ranking-based decisions.
• Existing models struggle with the dynamic nature of wind power, especially during high-fluctuation regimes.

Purpose of the Study:

• To develop a novel wind power forecasting framework that integrates ranking consistency and temporal smoothness.
• To address the limitations of traditional methods in handling ordered outputs for grid management tasks.
• To improve the accuracy and reliability of wind power predictions, particularly under varying wind conditions.

Main Methods:

• Developed a deep neural architecture utilizing attention mechanisms for end-to-end training.
• Introduced a composite multi-objective loss function to minimize prediction errors, maximize rank alignment, and enforce temporal rank regularization.
• Constructed a high-resolution dataset with synchronized SCADA, meteorological, and geographic data, including labeled wind regimes.

Main Results:

• The proposed model outperformed baseline methods (LSTM, Transformer, LambdaMART) in MAE, RMSE, and NDCG.
• Achieved significant improvements in forecasting accuracy, especially during low, ramping, and saturation wind regimes.
• Demonstrated up to 35% improvement in Temporal Rank Stability Index (TRSI) compared to state-of-the-art alternatives.

Conclusions:

• The novel multi-objective loss function enables ranking-aware and temporally robust wind forecasting.
• The new wind regime-labeled dataset facilitates comprehensive evaluation of forecasting and ranking capabilities.
• The findings pave the way for integrating rank-sensitive intelligence into practical grid-scale forecasting pipelines.