Prediction of wind power based on TabNet model under severe convective weather

Severe convective weather is often accompanied by extreme weather conditions such as storms, resulting in significant instability of wind speed and temperature parameters. This instability can lead to large changes in airflow patterns in the wind field, making it more complex to predict fan response and power output. Therefore, accurate power prediction of wind farm under severe convection weather is of great significance to economic dispatching and safe and stable operation of power system. In order to make full use of the effective information from multiple data sources to further improve the prediction accuracy of wind farm generation power under severe convection weather, a generation power prediction method based on TabNet model is proposed. The influencing factors of wind power under severe convection weather are analyzed, and the TabNet model is used to achieve feature selection, and the TabNet model is used to predict wind power under severe convection weather. The findings indicate that the suggested approach can successfully utilize multi-variable time series data during intense convective weather conditions to forecast wind farm power generation, demonstrating a high level of prediction accuracy.