全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

基于贪心核特征提取方法的中期峰值负荷预测

DOI: 10.13195/j.kzyjc.2013.0774, PP. 1661-1666

Keywords: 贪心算法,特征提取,核主元回归,核岭回归,负荷预报

Full-Text   Cite this paper   Add to My Lib

Abstract:

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.

References

[1]  杜杰, 徐立中, 曹一家, 等. 短期负荷预测Volterra 滤波器模型[J]. 控制与决策, 2009, 24(12): 1903-1908.
[2]  (Du J, Xu L Z, Cao Y J, et al. Short-term load forecasting model based on Volterra filter[J]. Control and Decision, 2009, 24(12): 1903-1908.)
[3]  Ghiassia M, Zimbrab D K, Saidane H. Medium term system load forecasting with a dynamic artificial neural network model[J]. Electric Power Systems Research, 2006, 76(5): 302-316.
[4]  Moody J, Darken C. Fasting learning in networks of locally-tuned processing units[J]. Neural Computation, 1989, 1(2): 281-294.
[5]  Elattar E E, Goulermas J Y, Wu Q H. Electric load fore-casting based on locally weighted support vector regression[J]. IEEE Trans on SMC, 2010, 40(4): 438-447.
[6]  Chen B J, Chang M W, Lin C J. Load forcasting using support vector machines: A study on eunite competition 2001[J]. IEEE Trans on Power Load Systems, 2004, 19(4): 1821-1830.
[7]  Saunders C, Gammerman A, Volk V. Ridge regression algorithm in dual variables[C]. Proc of the 15th Int Conf on Machine Learning. Madison-Wisconsin: Morgan Kaufmann Publishers, 1998: 515-521.
[8]  Scholkopf B, Smola A, Muller K. Nonlinear component analysis as a kernel eigenvalue problem[J]. Neural Computation, 1998, 10(5): 1299-1319.
[9]  Rosipal R. Kernel partial least squares for nnlinear regression and discrimination[J]. Neural Network World, 2003, 13(3): 291-300.
[10]  Rosipal R, Girolami M. An expectation-maximization approach to nonlinear component analysis[J]. Neural Computation, 2001, 13(3): 505-510.
[11]  Franc V, Hlavac V. Greedy algorithm for a training set reduction in the kernel methods[C]. Proc of Computer Analysis of Images and Patterns. Berlin: Springer, 2003: 426-433.
[12]  Franc V. Optimization algorithms for kernel methods[D]. Prague: Department of Cybernetics, Czech Technical University, 2005.
[13]  Sincak P. World-wide competition within the EUNITE network[EB/OL]. (2001-08-05)[2012-11-06]. http://neuron.tuke.sk/competition/index.php.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133