Day-Ahead Wind Power Prediction Using Clustering and GANs

The patent replaces the traditional physical-mechanism-based prediction system with a data-driven machine learning system. Specifically, it uses Long Short-Term Memory (LSTM) neural networks and attention mechanisms to process meteorological data and historical wind power data, substituting complex physical calculations with automated pattern recognition algorithms that achieve higher accuracy without manual intervention in feature engineering.

The patent transforms the prediction approach by changing from fixed physical parameter relationships to dynamic parameter learning. The system learns optimal parameter combinations and relationships from historical data, allowing the model to adapt to changing conditions. The attention mechanism dynamically weights different input parameters based on their relevance to current prediction needs, rather than using fixed physical formulas.

The patent implements preliminary action by pre-processing meteorological data and historical wind power data into standardized formats before model training. The system pre-features engineering is automatically performed during the training phase, where the LSTM network learns optimal feature representations from raw data. This eliminates the need for complex manual feature engineering during actual prediction operations, significantly improving efficiency.

The patent enables the prediction system to serve itself through automated model training and optimization. The system automatically adjusts hyperparameters, selects optimal model architectures, and refines predictions based on feedback from historical data without requiring complex manual intervention. The attention mechanism automatically identifies and weights important features, making the system self-optimizing rather than requiring continuous manual tuning.