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Title: The key technology for grid integration of wind power : direct probabilistic interval forecasts of wind power
Other Titles: 风电并网关键技术:风电的直接概率预测
Authors: Xu, Z 
Wan, C 
Keywords: Wind power
Probabilistic interval forecast
Extreme learning machine
Evolutionary computation
Issue Date: 2013
Publisher: 南方电网技术编辑部
Source: 南方电网技术 (Southern power system technology), 2013, v. 7, no. 5, p. 1-9 How to cite?
Journal: 南方电网技术 (Southern power system technology) 
Abstract: The wind power is an important renewable energy,but it has features of high volatility and uncertainty,therefore a large scale integration of wind power into power system will impose significant challenges in system operation. Accurate wind power prediction is one of the key technologies to reduce the risk of its grid integration. Because of the nonstationarities and nonlinearities of wind power series,traditional point prediction methods cannot provide satisfactory prediction results. In contrast,probabilistic interval based wind power forecasting techniques can simultaneously quantify the prediction error and the associated probability,thereby can more effectively support power system operation to cope with various uncertainties and risks. This paper firstly summarizes the latest developments in wind power forecasting techniques,then proposes an Extreme Learning Machine( ELM) and evolutionary computation based method to directly generate wind power prediction intervals. Compared to the existing methods,the advantage of the proposed method is able to directly generate prediction intervals through one optimization process,thus to largely simplify the model construction and avoid prediction errors analysis. The proposed method has been tested with practical wind farm data in Denmark,and the results demonstrate that it can efficiently and accurately provide probabilistic prediction intervals of wind power.
ISSN: 1674-0629
Rights: © 2013 中国学术期刊电子杂志出版社。本内容的使用仅限于教育、科研之目的。
© 2013 China Academic Journal Electronic Publishing House. It is to be used strictly for educational and research purposes.
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