Improving Wind Speed Forecast Accuracy with Transformer Architecture: A Deep Learning Approach

Wind energy is rapidly gaining recognition as a leading renewable energy source worldwide. Accurate forecasting of wind speed is essential for enhancing wind turbine performance, facilitating grid integration, and minimizing the uncertainties in wind energy generation. This paper presents a comparative analysis of different wind speed prediction models, specifically the Kalman Filter, Wavelet Kalman Filter, and Transformer-based models, evaluated on real-world wind speed data. To improve prediction accuracy, wavelet decomposition is used to isolate high-frequency fluctuations from the low-frequency components, creating a more refined model for wind speed forecasting. The models were trained and tested on data collected across all seasons (summer, autumn, winter, and spring) from various geographical locations, ensuring that the results are both robust and generalizable. The findings demonstrate that the proposed Transformer-based model outperforms traditional approaches such as the Kalman Filter and Wavelet Kalman Filter in both accuracy and computational efficiency. This new model achieves high prediction accuracy across all four seasons for both sites, indicating its broad applicability. The key innovation of this work lies in the development of a deep learning model based on the Transformer architecture, which effectively captures temporal dependencies and complex patterns in sequential wind data-insights that traditional models may overlook.