ULTRA-SHORT-TERM WIND POWER PREDICTION BASED ON SGMD-SE AND OPTIMIZED TCN-BiLSTM/BiGRU
To enhance the precision of ultra-short-term wind power prediction,a model is proposed that combines SGMD-SE with an optimized TCN-BiLSTM/BiGRU framework.Firstly,highly correlated variables are selected using maximum information coefficient(MIC)as input features.Secondly,symplectic geometry modal decomposition(SGMD)is utilized to decompose the original signal into initial symplectic geometry components(SGC),effectively suppressing mode mixing and eliminating the need for decomposition parameters.Then,sample entropy(SE)is applied to reconstruct the initial components,which are classified into high and low complexity categories.Based on the different characteristics of these categories,separate TCN-BiLSTM and TCN-BiGRU models are built for prediction.To improve the predictive performance of BiLSTM and BiGRU,time convolutional networks(TCN)are utilized to extract features from the two component categories.Additionally,an improved optimization algorithm called IOOA,based on Tent chaotic mapping and Cauchy mutation,is proposed to optimize their key parameters.Finally,the final prediction result is superimposed by combining the predicted values of each component.The findings suggest that the proposed hybrid prediction model significantly enhances the accuracy of ultra-short-term wind power forecasting and holds substantial practical utility.
wind power predictiondecompositionlong short-term memorytime convolutional networkosprey optimization algorithm
万方数据
维普
