Short-term wind power forecasting using hybrid method based on enhanced boosting algorithm

Author:

JIANG YuORCID,CHEN Xingying,YU Kun,LIAO Yingchen

Funder

The National High Technology Research and Development of China 863 Program

The National Natural Science Foundation of China

The State Grid Corporation of China (Impact Research of Source-Grid-Load Interaction on Operation and Control of Future Power System

Publisher

Springer Science and Business Media LLC

Subject

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

Reference25 articles.

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2. Ridge R (2012) 2011 wind technologies market report. DOE/GO-102012-347, Energy Efficiency and Renewable Energy, US Department of Energy (DOE), Washington, DC, USA

3. Wu YK, Hong JS (2007) A literature review of wind forecasting technology in the world. In: Proceedings of the 2007 IEEE Lausanne power tech conference, Lausanne, Switzerland, 1–5 Jul 2007, pp 504–509

4. Kehler J, Hu M, McMullen M et al (2010) ISO perspective and experience with integrating wind power forecasts into operations. In: Proceedings of the 2010 IEEE power and energy society general meeting, Minneapolis, MN, USA, 25–29 Jul 2010, 5 pp

5. Bilgili M, Sahin B (2010) Comparative analysis of regression and artificial neural network models for wind speed prediction. Meteorol Atmos Phys 109(1/2):61–72

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