一种基于多信息融合的风电功率预测特征选择方法

A wind power prediction feature selection method based on multi-information fusion

风电功率波动性强、随机性大,机组监测数据变化复杂.为了提高风电功率预测的准确性,提出一种基于多信息度量融合(MIMF)的风电功率预测特征选择方法.在对决策树、L_(1)正则化和递归特征消除这三种典型的特征选择方法进行分析研究的基础上,综合决策树可以清晰表达特征的重要性、L_(1)正则化避免过拟合和递归特征消除考虑特征间相关性的特点,通过将这三种方法所选特征取合集并依据各特征相关性选出用于风电功率预测的特征集,构造了一种融合决策树、L_(1)正则化和递归特征消除三种特征选择内在信息度量的特征选择方法,对所构建的融合多信息度量的特征选择方法进行了仿真分析.在某风电场实测数据上的仿真结果表明,与采用单一特征选择方法相比,该方法可有效提高风电功率的预测精度.

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 selection method based on multi-information metrics fusion(MIMF)is proposed.Based on the analysis of three typical feature selection methods,including decision tree,L_(1)-regularization,and recursive feature elimination,it was determined that while decision trees offer clarity in expressing feature importance,L_(1)-regularization mitigates overfitting,and recursive feature elimination accounts for inter-feature correlations.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 intercorrelations.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.