Ultra Short Term Wind Power Prediction Based on VMD-IMPA-SVM
Aiming at the problem of low prediction accuracy caused by the strong volatility of wind power generation,a combined prediction model based on variable modal decomposition(VMD),gray wolf optimization algorithm(GWO),Marine Predator Algorithm(MPA)and support vector machine(SVM)is constructed.Firstly,GWO is used to optimize the modal number and penalty factor of VMD,decompose the original power sequence into subsequence for noise reduction,improve the population generation and variation of MPA by using the methods of opposition learning and Cauchy's variance,and derive the Improved Marine Predator Algorithm(IMPA)to optimize the SVM.The kernel function and penalty parameters are optimized in SVM,and a combined VMD-IMPA-SVM prediction model is constructed,which predicts each subsequence and superimposes to obtain the final prediction value.Practical examples show that the proposed combined prediction model has high prediction accuracy and strong robustness.
wind power predictionvariable modal decompositionmarine predator algorithmsupport vector machinegray wolf optimization algorithm
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