Wind Power Power Interval Prediction Based on SVMD-Informer-XGBoost
In order to improve the accuracy of wind power interval prediction,this paper propo-ses a combination of successive variational mode decomposition(SVMD),Informer and XG-Boost algorithm models to achieve wind power interval prediction.First,the SVMD algorithm adaptively decomposes each modal component(IMF)and residual component of wind power da-ta.Secondly,the Informer and XGBoost model algorithms are optimized through the grid search algorithm(CV),and the optimal weight of the Informer-XGBoost output prediction value is de-termined through the grid search algorithm to build a prediction model based on SVMD-In-former-XGBoost.Finally,the quantile regression method is used to output the error between the predicted value and the true value to obtain the interval prediction of wind power.Experimental results show that the proposed method has significantly improved the accuracy and stability of interval prediction compared with the traditional single model.At the 95％confidence level,the interval prediction index interval coverage(PICP)and interval average width(PINAW)can reach 94.96％and 0.0362 respectively.
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