The precision of wind power prediction plays a vital role in ensuring the stable operation of wind power systems. To elevate this accuracy and enhance the real-time performance, this paper proposes a hybrid Support Vector Machine (SVM) method, with using the Long Short-Term Memory Network (LSTM) to categorize the wind data based on the statistical features and feedback to the trained SVM model. The hybrid Harris Hawk Optimization (HHO) SVM method adopts the Neuralprophet algorithm to model the seasonality of wind power data and then uses the Pelt technique to the LSTM aided Pelt-Neuralprophet HHO-SVM (i.e., PN-HHO-SVM) scheme, which can utilize the seasonal fluctuations inherent in wind power data. The Neuralprophet algorithm is employed to formulate the seasonal regression model of wind power data. Then, the Pelt technique is used to process the modeled data to locate the change points, so as to classify the time series with similar statistical properties. Furthermore, to tune the SVM hyperparameters for each identified cluster, the HHO algorithm is adopted. Consequently, the LSTM aided PN-HHO-SVM is achieved. The real data sourced from the National Renewable Energy Laboratory (NREL) are used for validation case studies, demonstrating the superiorities of the prediction performance, especially in distinguishing and discriminating the seasonal dynamics of wind power data characters.
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