A review on short-term and ultra-short-term wind power prediction

被引：62
|
作者：
Xue, Yusheng [1 ,2 ]
Yu, Chen [1 ,2 ]
Zhao, Junhua [3 ]
Li, Kang [4 ]
Liu, Xueqin [4 ]
Wu, Qiuwei [5 ]
Yang, Guangya [5 ]
机构：
[1] NARI Group Corporation (State Grid Electric Power Research Institute), Nanjing
[2] School of Automation, Nanjing University of Science and Technology, Nanjing
[3] College of Electrical Engineering, Zhejiang University, Hangzhou
[4] Queen's University Belfast, Northern Ireland
[5] Technical University of Denmark, Lyngby
来源：
Dianli Xitong Zidonghua/Automation of Electric Power Systems | 2015年 / 39卷 / 06期
关键词：
Combinational prediction; Evaluation index; Information flow; Probabilistic forecasting; Wind power prediction;
D O I：
10.7500/AEPS20141218003
中图分类号：
学科分类号：
摘要：
The impact of wind power prediction (WPP) on power systems is discussed and the factors affecting the accuracy of WPP are summarized. Then the paper unscrambles the WPP process from the viewpoint of information flow, classifies its research status and discusses the requirements of evaluation index for WPP results. It is proposed that the error evaluation index should reflect the WPP quality of the whole time window, and possible breakthroughs of WPP are also predicted. ©, 2015, State Grid Electric Power Research Institute Press. All right reserved.
引用
收藏
页码：141 / 151
页数：10
相关论文
共 90 条
  • [31] 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)
  • [32] Alexiadis M.C., Dokopoulos P.S., Sahsamanoglou H.S., Et al., Short term forecasting of wind speed and related electrical power, Solar Energy, 63, 1, pp. 61-68, (1998)
  • [33] Brown B.G., Katz R.W., Murphy A.H., Time series models to simulate and forecast wind speed and wind power, Journal of Climate and Applied Meteorology, 23, 8, pp. 1184-1195, (1984)
  • [34] Rajagopalan S., Santoso S., Wind power forecasting and error analysis using the autoregressive moving average modeling, IEEE Power & Energy Society General Meeting, (2009)
  • [35] Kamal L., Jafri Y.Z., Time series models to simulate and forecast hourly averaged wind speed in Wuetta, Pakistan, Solar Energy, 61, 1, pp. 23-32, (1997)
  • [36] Billinton R., Chen H., Ghajar R., A sequential simulation technique for adequacy evaluation of generating systems including wind energy, IEEE Trans on Energy Conversion, 11, 4, pp. 728-734, (1996)
  • [37] Rajesh G.K., Seetharaman K., Day-ahead wind speed forecasting using f-ARIMA models, Renewable Energy, 34, 5, pp. 1388-1393, (2009)
  • [38] Carpinone A., Langella R., Tests A., Et al., Very short-term probabilistic wind power forecasting based on Markov chain models, IEEE 11th International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), (2010)
  • [39] Jafazadeh S., Fadali S., Evrenosoglu C.Y., Et al., Hour-ahead wind power prediction for power systems using hidden Markov models and Viterbi algorithm, IEEE Power and Energy Society General Meeting, (2010)
  • [40] Bossanyi E.A., Short-term wind prediction using Kalman filters, Wind Engineering, 9, 1, pp. 1-8, (1985)
123456789