基于贪心核特征提取方法的中期峰值负荷预测  

Greedy kernel feature extraction method for medium term electricity peak load forecasting

作　　者：李军[1] 

机构地区：[1]兰州交通大学自动化与电气工程学院,兰州730070

出　　处：《控制与决策》2014年第9期1661-1666,共6页Control and Decision

基　　金：甘肃省高等学校基本科研业务费专项资金项目(620026);甘肃省教育厅硕导项目(1104-09)

摘　　要：针对中期电力负荷预测,提出基于贪心核主元回归(GKPCR)、贪心核岭回归(GKRR)的特征提取建模方法.通过对核矩阵的稀疏逼近,GKPCR和GKRR两种贪心核特征提取方法旨在寻找特征空间中数据的低维表示,计算需求低,适用于大数据集的在线学习.将所提出的方法应用于不同地区的电力负荷中期峰值预测,并与现有预测方法进行了比较.实验结果表明,在同等条件下,所提出的方法能有效地改进预测精度,而且性能更好,显示了其有效性和应用潜力.For midterm electricity load forecasting, modeling methods of feature extraction based on greedy kernel principal component regression（GKPCR） as well as greedy kernel ridge regression（GKRR） are proposed. On the basis of sparse approximation of the kernel matrix, the proposed greedy kernel feature extraction methods aim to find a lower dimensional representation of data embedded in the feature space. Modeling methods of greedy kernel feature extraction have low computational requirements and allow on-line processing of large data sets. The proposed GKPCR and GKRR methods are then applied to electricity peak load forecasting instances in different areas. Compared to existing other kernel-based forecasting methods, experimental results show that, the employed methods may significantly improve the accuracy of peak load forecasting under the same condition, which have considerably better performance, and show the effectiveness and applicability.

关 键 词：贪心算法 特征提取 核主元回归 核岭回归 负荷预测 

分 类 号：TN911[电子电信—通信与信息系统]