Tuning data preprocessing techniques for improved wind speed prediction

Abstract

Accurate wind speed forecasting is crucial for efficiently integrating of wind power into the electrical grid and ensuring a stable power supply. However, wind speed is inherently noisy and unpredictable, making it challenging to forecast accurately. This study investigates the effects of hyperparameters of Discrete Wavelet Transform (DWT) and Singular Spectrum Analysis (SSA) on the accuracy of wind speed forecasting using various prediction models. Our proposed method focuses on the optimisation of hyperparameters within the existing models, suggesting that significant untapped potential remains. Our study examines a wide range of wavelet function orders for DWT and varying trend ratio parameter for SSA, and evaluates their impact on the prediction accuracy using real data from thirteen locations in Jordan. Particularly, our investigation reveals that high-order Daubechies wavelets in DWT outperform low-order wavelets. The study also illustrates that optimal hyperparameters must be modified when changing the prediction model and the combination of DWT and SSA enhances prediction performance when the trend ratio is set to 90%. Our results demonstrate that fine-tuning data preprocessing techniques is essential for accurate wind speed prediction since hyperparameter tuning results in greater improvements in prediction accuracy than sophisticated prediction models alone. Our findings underscore the importance of leveraging data preprocessing techniques and hyperparameter tuning for accurate wind speed forecasting.