摘要
One of the cornerstones for guaranteeing the stability of wind generation and electric power system operation is wind speed prediction.This research offers a method based on Particle Swarm Optimization(PSO)to optimize the Bidirectional Long Short⁃term Memory Network(BiLSTM)in order to improve the wind speed prediction accuracy,taking into account the highly stochastic and regular aspects of wind speed.Firstly,the wind speed time sequence is subjected to the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN).The complexity of the wind speed pattern is reduced by decomposing it into components with different local feature information.The BiLSTM model,which incorporates the attention mechanism for prediction,is then fitted to the decomposed data,and its parameters are optimized using the particle swarm technique,reducing errors in predictive modeling.To get the final prediction,the components are finally superimposed.The empirical evidence shows that the CEEMDAN⁃PSO⁃BiLSTM⁃attention model decreases the RMSE(Root⁃Mean⁃Square⁃Error)by 15%-44%,the MAE by 18%-45%,the MAPE by 24%-52%,and the R2 by 1.4%-2.7%in comparison to the BiLSTM and other models.The validation of CEEMDAN⁃PSO⁃BiLSTM⁃attention model in short⁃term wind speed prediction is verified.
基金
Sponsored by Science Research Project of Liaoning Education Department(Grant No.LJKZ0143)
Open Project of State Key Laboratory of Syn⁃thetical Automation for Process Industries(Grant No.2023⁃kfkt⁃01).
