Wind power prediction based on variational mode decomposition multi-frequency combinations-Reference-Cited by-同舟云学术

Wind power prediction based on variational mode decomposition multi-frequency combinations

Published:2018-12-12 Issue:2 Volume:7 Page:281-288
ISSN:2196-5625
Container-title:Journal of Modern Power Systems and Clean Energy
language:en
Short-container-title:J. Mod. Power Syst. Clean Energy

Author:

ZHANG Gang^ORCID,LIU Hongchi,ZHANG Jiangbin,YAN Ye,ZHANG Lei,WU Chen,HUA Xia,WANG Yongqing

Funder

the National Key Research and Development Program of China

the National Natural Science Foundation of China under Grant

the Shaanxi provincial education office fund

Publisher

Springer Science and Business Media LLC

Subject

Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment

Link

http://link.springer.com/content/pdf/10.1007/s40565-018-0471-8.pdf

Reference40 articles.

1. Liu B, He ZJ (2016) Wind power status and development trends. J Northeast Dianli Univ 36(2):7–13

2. Xue YS, Yu C (2015) A review on short-term and ultra-short-term wind power forecasting. Autom Electr Power Syst 39(6):141–151

3. Shi YW, Pan XP (2014) Short-term wind speed forecasting considering historical meteorological data. Electr Power Autom Equip 34(10):75–80

4. Dong L, Wang LX (2008) Modeling and analysis of prediction of wind power generation in the large wind farm based on chaotic time series. Trans China Electrotech Soc 23(12):125–129

5. Yang XY, Xiao Y (2005) Wind speed and generated power forecasting in wind farm. Proc CSEE 11:1–5

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