Accurate short-term wind power forecast technique plays a crucial role in maintaining the safety and economic efficiency of smart *** numerous studies have employed various methods to forecast wind power,there remains...
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Accurate short-term wind power forecast technique plays a crucial role in maintaining the safety and economic efficiency of smart *** numerous studies have employed various methods to forecast wind power,there remains a research gap in leveraging swarm intelligence algorithms to optimize the hyperparameters of the Transformer model for wind power *** improve the accuracy of short-term wind power forecast,this paper proposes a hybrid short-term wind power forecast approach named STL-IAOA-itransformer,which is based on seasonal and trend decomposition using LOESS(STL)and itransformer model optimized by improved arithmetic optimization algorithm(IAOA).First,to fully extract the power data features,STL is used to decompose the original data into components with less redundant *** extracted components as well as the weather data are then input into itransformer for short-term wind power *** final predicted short-term wind power curve is obtained by combining the predicted *** improve the model accuracy,IAOA is employed to optimize the hyperparameters of *** proposed approach is validated using real-generation data from different seasons and different power stations inNorthwest China,and ablation experiments have been ***,to validate the superiority of the proposed approach under different wind characteristics,real power generation data fromsouthwestChina are utilized for *** results with the other six state-of-the-art prediction models in experiments show that the proposed model well fits the true value of generation series and achieves high prediction accuracy.
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