Wind Speed Forecasting for Wind Power Production Based on Frequency-Enhanced Transformer

Wind power plays a more and more important part in today’s electricity market with the increasing need of clean energy. How to make better use of wind power is attracting great attention due to the inherent uncertainty caused by extreme complexity of weather systems. In order to address difficulty, we introduced a time-series prediction method called FEDformer to the field of wind power forecasting, and made some adjustment so that the FEDformer could perform better in this field. We tested this novel model on a real-world wind farm dataset, and compared it with other traditional machine learning methods. Results show that the proposed method for wind power prediction has better accuracy and greater potential for engineering applications. It is effective to apply transformer-based model to the field of wind power prediction.