Machine learning-based wind speed prediction using random forest: a cross-validated analysis for renewable energy applications

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Abstract
Wind speed prediction plays a crucial role in renewable energy planning and optimization. This study presents a comprehensive analysis of wind speed forecasting using Random Forest (RF) models. The research utilized high-resolution wind speed data collected throughout 2023 at the Bowen Abbot facility. Our methodology employed a RF with cross-validation techniques to ensure model stability and reliability. The model demonstrated robust performance across multiple evaluation metrics, achieving an average R² score of 0.9155 (±0.0035) through 5-fold cross-validation. Error analysis revealed consistent performance across training, testing, and validation sets, with root mean square errors (RMSE) of 0.6624 (±0.0098) m/s. Feature importance analysis revealed that the 3-hour rolling mean wind speed was the most influential predictor, accounting for 89.84% of the model's predictive power, followed by 1-hour (2.59%) and 3-hour (2.57%) lagged wind speeds. This hierarchical importance of temporal features suggests that recent wind patterns are crucial for accurate predictions. The error distribution analysis showed approximately normal distributions with slight deviations in the tails, particularly in the validation set (kurtosis: 5.2146). Key findings indicate that the model maintains high prediction accuracy across different temporal scales, with mean absolute errors (MAE) averaging 0.4998 (±0.0098) m/s. The model's stability across different data partitions suggests its reliability for operational deployment. These results demonstrate the potential of RF algorithms for accurate wind speed forecasting in renewable energy applications, providing a valuable tool for wind power generation planning and management. The study's findings contribute to the growing body of research on machine learning applications in renewable energy, offering insights into model performance evaluation and error analysis methodologies for wind speed prediction systems.

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