A numerical weather prediction feature selection approach based on minimal-redundancy-maximal-relevance strategy for short-term regional wind power prediction

被引：0

作者：

Zhao, Yongning ^{[1
]}

Ye, Lin ^{[1
]}

机构：

[1] College of Information and Electrical Engineering, China Agricultural University, Haidian District, Beijing

来源：

Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering | 2015年 / 35卷 / 23期

基金：

中国国家自然科学基金;

关键词：

Feature selection; Mutual information; Numerical weather prediction; Regional prediction; Wind power;

D O I：

10.13334/j.0258-8013.pcsee.2015.23.004

中图分类号：

学科分类号：

摘要：

Regional wind power prediction is of great significance for large-scale wind power integration. Normally regional prediction error is less than that of an individual wind farm due to the spatial smoothing effect. In this paper, a feature selection method of minimal-redundancy-maximal-relevance (mRMR) based on mutual information theory was presented to select optimal feature subset from available numerical weather prediction (NWP) variables for regional wind power prediction. The selected optimal feature subset could maximize relevant information and minimize redundant information and noises from numerous original NWP datasets. An artificial neural network model was adopted to predict regional wind power to verify the validity of the selected optimal feature subset. As well, influence of subset cardinality on prediction accuracy was analyzed. Case study indicates that the optimal subset with a small number of features can not only improve regional prediction accuracy effectively than existing conventional methods, but also reduce computational cost and data-resource dependency significantly. In addition, the relationship between selected optimal feature subset and the spatial distribution of wind farms was investigated. Results show that the proposed approach can be used for improving the accuracy of the regional wind power prediction practically. © 2015 Chin. Soc. for Elec. Eng.

引用

页码：5985 / 5994

页数：9

共 25 条

[1]

Yang X., Xiao Y., Chen S., Wind speed and generated power forecasting in wind farm, Proceedings of the CSEE, 25, 11, pp. 1-5, (2005)

[2]

Liu D., Guo J., Huang Y., Et al., Dyanmic economic dispatch of wind integrated power system based on wind power probabilistic forecasting and operation risk constraints, Proceedings of the CSEE, 33, 16, pp. 9-15, (2013)

[3]

Zhang L., Ye T., Xin Y., Et al., Problems and measures of power grid accommodating large scale wind power, Proceedings of the CSEE, 30, 25, pp. 1-9, (2010)

[4]

Tao J., Zhang H., Zhu G., Zhao M., Et al., Wind power prediction based on technology of advanced phase space reconstruction, Proceedings of the CSEE, 31, 28, pp. 9-14, (2011)

[5]

Ye L., Liu P., Combined model based on emd-svm for short-term wind power prediction, Proceedings of the CSEE, 31, 31, pp. 102-108, (2011)

[6]

Tarroja B., Mueller F., Eichman J.D., Et al., Spatial and temporal analysis of electric wind generation intermittency and dynamics, Renewable Energy, 36, 12, pp. 3424-3432, (2011)

[7]

Focken U., Lange M., Monnich K., Et al., Short-term prediction of the aggregated power output of wind farms: a statistical analysis of the reduction of the prediction error by spatial smoothing effects, Journal of Wind Engineering and Industrial Aerodynamics, 90, 3, pp. 231-246, (2002)

[8]

Ye L., Zhao Y., A review on wind power prediction based on spatial correlation approach, Automation of Electric Power Systems, 38, 14, pp. 126-135, (2014)

[9]

Focken U., Lange M., Waldl H.P., Reduction of wind power prediction error by spatial smoothing effects, Proceedings of the European Wind Energy Conference, pp. 822-825, (2001)

[10]

Bai Y., Fang D., Hou Y., Et al., Regional wind power forecasting system for inner mongolia power grid, Power System Technology, 34, 10, pp. 157-162, (2010)

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