Short-term probabilistic forecasting based on KPCA-KMPMR for wind power

被引：0

作者：

Li J. ^{[1
]}

Chang Y. ^{[1
]}

机构：

[1] School of Automation & Electrical Engineering, Lanzhou Jiaotong University, Lanzhou

来源：

Dianli Zidonghua Shebei/Electric Power Automation Equipment | 2017年 / 37卷 / 02期

基金：

中国国家自然科学基金;

关键词：

Kernel minimax probability machine regression; Kernel principal component analysis; Probabilistic forecasting; Wind power;

D O I：

10.16081/j.issn.1006-6047.2017.02.004

中图分类号：

学科分类号：

摘要：

A probabilistic forecasting method of short-term wind power based on the combination of KPCA (Kernel Principal Component Analysis) and KMPMR(Kernel Minimax Probability Machine Regression) is proposed, which applies KPCA to pre-process the data for the effective extraction of the nonlinear principal component from the feature space as the input of forecasting model. Assuming the mean and covariance matrix of the distribution which generates the forecasting model are known, the KMPMC method regards the classification hyperplane of KMPMC as the output of forecasting model for maximizing the minimum probability of the model output within the boundary of its true value. Experimental results show that, the proposed method has better forecasting accuracy than the existing forecasting methods and it can provide the probability distribution of forecasting error. © 2017, Electric Power Automation Equipment Press. All right reserved.

引用

页码：22 / 28and36

页数：2814

共 19 条

[1]

Wang X.C., Guo P., Huang X.B., A review of wind power forecasting model, Energy Procedia, 12, 39, pp. 770-778, (2011)

[2]

Jung J., Broadwater R.P., Current status and future advances for wind speed and power forecasting, Renewable and Sustainable Energy Reviews, 31, 2, pp. 762-777, (2014)

[3]

Choudhary A.K., Upadhyay K.G., Tripathi M.M., Estimation of wind power using different soft computing methods, International Journal of Electrical Systems, 1, 1, pp. 1-7, (2011)

[4]

Foley A.M., Leahy P.G., Marvuglia A., Et al., Current methods and advances in forecasting of wind power generation, Renewable Energy, 37, 1, pp. 1-8, (2012)

[5]

Chen D., Gong Q., Jin Z., Et al., Short-term wind power prediction based on support vector regression machine optimized by adaptive disturbance quantum-behaved particle swarm optimization, Power System Technology, 37, 4, pp. 974-980, (2013)

[6]

Wang L., Dong L., Liao X., Et al., Short-term power prediction of a wind farm based on wavelet analysis, Proceedings of the CSEE, 29, 28, pp. 30-33, (2009)

[7]

Fan G., Wang W., Liu C., Et al., Wind power prediction based on artificial neural network, Proceedings of the CSEE, 28, 34, pp. 118-123, (2008)

[8]

Ling W., Hang N., Li R., Short-term wind power forecasting based on cloud SVM model, Electric Power Automation Equipment, 33, 7, pp. 34-38, (2013)

[9]

Zeng J.W., Qiao W., Short-term wind power prediction using a wavelet support vector machine, IEEE Transactions on Sustainable Energy, 3, 2, pp. 255-264, (2012)

[10]

Zhang L., Lu J., Mei Y., Et al., Wind power forecasting based on different optimization criterions, Electric Power Automation Equipment, 35, 5, pp. 139-145, (2015)

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