Short-Term Wind Power Prediction Method Based on Combination of Meteorological Features and CatBoost

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 Cat Boost 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 Cat Boost 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 Cat Boost not only improve the prediction accuracy of short-term wind power,but also have higher calculation efficiency.