Analysis of Learning-based Offshore Wind Power Prediction Models with Various Feature Combinations
This work addresses the problem of accurate wind power forecasting for offshore wind farm design and site selection, but it is incremental as it applies existing methods to new data with specific feature analysis.
This paper investigated machine learning models for predicting offshore wind power near the Gulf of Mexico, finding that using wind speed as the output feature improved prediction accuracy by about 10% compared to using wind power directly, while multi-feature inputs showed limited benefits due to poor feature correlations and model generalization issues.
Accurate wind speed prediction is crucial for designing and selecting sites for offshore wind farms. This paper investigates the effectiveness of various machine learning models in predicting offshore wind power for a site near the Gulf of Mexico by analyzing meteorological data. After collecting and preprocessing meteorological data, nine different input feature combinations were designed to assess their impact on wind power predictions at multiple heights. The results show that using wind speed as the output feature improves prediction accuracy by approximately 10% compared to using wind power as the output. In addition, the improvement of multi-feature input compared with single-feature input is not obvious mainly due to the poor correlation among key features and limited generalization ability of models. These findings underscore the importance of selecting appropriate output features and highlight considerations for using machine learning in wind power forecasting, offering insights that could guide future wind power prediction models and conversion techniques.