Wind Power Prediction Algorithm based on Random Forest and Support Vector Regression
To achieve accurate wind power output prediction and ensure the stable grid connection of wind power generation systems,an accurate wind power prediction algorithm based on the random forest model and support vector regression model was proposed.The algorithm was based on regression trees and random forest models to evaluate the importance of factors affecting wind power generation;based on fea-ture selection theory,an optimal feature set was constructed;the optimal feature set was input into the support vector regression model to predict wind power generation.In order to verify the validity of the al-gorithm,this paper used actually measured data to carry out experimental analysis.Experimental results show that compared to using the random forest model alone,the algorithm significantly improves the pre-diction accuracy with a reduction of 19.67%in average absolute error;compared to the long short-term memory neural network model,the algorithm achieves the same high accuracy while significantly reducing the model complexity and training time required.The algorithm can achieve accurate wind power predic-tion,which has important theoretical and practical significance.
wind power generationpower predictionsupport vector regressionrandom forest model
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万方数据
