基于KPCA-KMPMR的短期风电功率概率预测

发布时间：2018-06-08 05:19

  本文选题：核主成分分析 + 核最小最大概率回归机　； 参考：《电力自动化设备》2017年02期

【摘要】：针对短期风电功率概率预测,提出一种基于核主成分分析(KPCA)与核最小最大概率回归机(KMPMR)相结合的方法。KPCA方法可对数据进行预处理,在特征空间中有效提取模型输入的非线性主元;KMPMR方法在仅需假定产生预测模型的数据分布的均值与协方差矩阵已知时,将最小最大概率分类机(KMPMC)的分类超平面看作预测模型的输出,可最大化模型的输出位于其真实值边界内的最小概率。实验结果表明,所提方法在预测精度上优于现有的预测方法,并能提供预测误差的分布范围。
[Abstract]:For short-term wind power probability prediction, a method based on kernel principal component analysis (KPCA) and kernel minimum and maximum probability regression (KMPMRs) is proposed. KPCA can preprocess the data. The nonlinear principal component KMPMR method, which effectively extracts the input from the model in the feature space, only needs to assume that the mean value and covariance matrix of the data distribution generated by the prediction model are known. The classification hyperplane of the minimum maximum probability classifier (KMPMC) is regarded as the output of the prediction model, which maximizes the minimum probability that the output of the model lies within its real value boundary. The experimental results show that the proposed method is superior to the existing prediction methods in prediction accuracy and can provide the distribution range of prediction errors.
【作者单位】： 兰州交通大学自动化与电气工程学院;
【基金】：国家自然科学基金资助项目(51467008)~~
【分类号】：TM614
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本文编号：1994675