LAUSR

As one of the hot topics in the field of new energy, short-term wind power prediction research should pay attention to the impact of meteorological characteristics on wind power while improving the prediction accuracy. Therefore, a short-term wind power prediction method based on the combination of meteorological features and CatBoost is presented. Firstly, morgan-stone algebras and sure independence screening(MS-SIS) method is designed to filter the meteorological features, and the influence of the meteorological features on the wind power is explored. Then, a sort enhancement algorithm is designed to increase the accuracy and calculation efficiency of the method and reduce the prediction risk of a single element. Finally, a prediction method based on CatBoost network is constructed to further realize short-term wind power prediction. The National Renewable Energy Laboratory (NREL) dataset is used for experimental analysis. The results show that the short-term wind power prediction method based on the combination of meteorological features and CatBoost not only improve the prediction accuracy of short-term wind power, but also have higher calculation efficiency.