Short-term wind power prediction based on extreme learning machine with error correction-Reference-Cited by-同舟云学术

Short-term wind power prediction based on extreme learning machine with error correction

Published:2016-06-20 Issue:1 Volume:1 Page:
ISSN:2367-2617
Container-title:Protection and Control of Modern Power Systems
language:en
Short-container-title:Prot Control Mod Power Syst

Author:

Li Zhi,Ye Lin,Zhao Yongning,Song Xuri,Teng Jingzhu,Jin Jingxin

Publisher

Springer Science and Business Media LLC

Subject

Electrical and Electronic Engineering,Energy Engineering and Power Technology,Safety, Risk, Reliability and Quality

Link

http://link.springer.com/content/pdf/10.1186/s41601-016-0016-y.pdf

Reference23 articles.

1. Juban, J, Sibert, N, Kariniotakis, GN (2007). Probabilistic short-term wind power forecasting for the optimal management of wind generation (pp. 683–688). Lausanne: IEEE Powertech.

2. Giebel, G, Brownsword, R, Kariniotakis, G, Denhard, M, Draxl, C. The state of the art in short-term prediction of wind power a literature overview. Deliverable Report of the Anemos Project, 2011; Available from: http://orbit.dtu.dk/en/publications/the-stateoftheart-in-shortterm-prediction-of-wind-power(0d76e147-4bfc-444b-af00-13a0db8f132e).html . Accessed 19 May 2016.

3. Deob, MC, Ghosh, S, Kulkarnia, S. Effect of Climate Change on Wind Persistence at Selected Indian Offshore Locations. 8th International Conference on Asian and Pacific Coasts (APAC 2015): Indian Institute of Technology Madras. Volume 116. pp. 615–622. http://www.apac2015.com/

4. Agüera-Pérez, A, Palomares-Salas, JC, González de laRosa, JJ, Moreno-Muñoz, A. (2013). Spatial persistence in wind analysis. Journal of Wind Engineering and Industrial Aerodynamics, 119, 48–52.

5. Sideratos, G, Hatziargyriou, ND. (2007). An advanced statistical method for wind power forecasting. IEEE Transactions on Power Systems, 22(1), 258–265.

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