基于数据挖掘的风电功率预测特征选择方法

Feature selection method for wind power prediction based on data mining

输入特征向量的选择是建立风电功率预测模型中至关重要的第一步,但由于风电机组的待选监测量项目过多、部分监测量与风电功率的相关性不明显甚至不相关、信息冗余量大等因素造成输入向量集的选取不够合理,进一步影响功率预测模型的准确性。针对这一问题,在综合对比研究了邻域粗糙集、随机森林和互信息这三种较为有效的用于特征选择的数据挖掘算法的基础上,提出了一种综合性能较好的基于随机森林筛选风电功率预测模型输入向量的方法,并分析了另两种方法的特点和适用范围,最后使用风机的实际运行数据,基于最小二乘支持向量回归算法对文中所提出的方法进行了验证。仿真结果表明,该方法能够通过减少功率预测模型的输入向量有效地降低模型复杂度,不仅加快了模型的预测速度而且提高了预测的精度。

The selection of input feature vector is the first important step in the establishment of wind power prediction model,but due to the excessive monitor items,the correlation between partial monitor items and wind power is not obvious or even irrelevant,and the redundancy information causes the selection of input vector set is not reasonable,so the accuracy of the power prediction model is affected. In order to solve this problem,three effective data mining algorithms for feature selection,namely neighborhood rough set,random forest and mutual information,are studied synthetically. And a new method based on random forest for selecting input vectors of wind power prediction model with better comprehensive performance is proposed,and the characteristics and application range of other methods are analyzed. Finally,the proposed method is validated based on the least squares support vector regression algorithm through using the actual operation data of the turbine. The simulation results show that this method can effectively reduce the complexity of the model by reducing the input vectors,which not only speeds up the prediction speed,but also improves the prediction accuracy of the model.