Wind power has strong volatility and randomness,and the changes in unit monitoring data are complex.In order to improve the accuracy of wind power prediction,a wind power prediction feature se-lection method based on multi-information metrics fusion(MIMF)is proposed.Based on the analysis of three typical feature selection methods,including decision tree,L1-regularization,and recursive feature elimination,it was determined that while decision trees offer clarity in expressing feature importance,L1-regularization mitigates overfitting,and recursive feature elimination accounts for inter-feature correla-tions.Leveraging the strengths of these methods,a composite feature selection framework was devised,wherein the union of features selected by each technique is further refined based on their respective inter-correlations.Subsequently,the constructed feature selection method fused with multi-information metrics is simulated and analyzed.The simulation results on the measured data of a wind farm show that compared with the single feature selection method,this method can effectively improve the prediction accuracy of wind power.
维普
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