Long-Term Wind Power Prediction Based on Rough Set

Abstract:

In long-term wind power prediction, dealing with the relevant factors correctly is the key point to improve the prediction accuracy. This paper presents a prediction method with rough set analysis. The key factors that affect the wind power prediction are identified by rough set theory. The chaotic characteristics of wind speed time series are analyzed. The rough set neural network prediction model is built by adding the key factors as the additional inputs to the chaotic neural network model. Data of Fujin wind farm are used for this paper to verify the new method of long-term wind power prediction. The results show that rough set method is a useful tool in long-term prediction of wind power.

• Periodical: Applied Mechanics and Materials (Volume 329)
• Pages: 411-415
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.329.411
• Online since: June 2013
• Authors: Shuang Gao, Lei Dong, Xiao Zhong Liao, Yang Gao
• Keywords: Long-Term Prediction, Neural Network (NN), Rough Set (RS), Wind Power Prediction

References

[1] Information on http://www.gwec.net

[2] Lei Ma, Shiyan Luan, Chuanwen Jiang: A review on the forecasting of wind speed and generated power, Renewable and Sustainable Energy Reviews, Vol.13, No.4 (2009), pp.915-920

DOI: 10.1016/j.rser.2008.02.002

[3] Duoqian Miao, Daoguo Li, in: Rough set theory, algorithm and application, Tsinghua University Press, Beijing, China (2008), in press.

[4] R. Rape, D. Fefer, and J. Drnovsek, Time series prediction with neural networks：a case study of two examples, Instrumentation and Measurement Technology Conference, Vol.1 (1994), pp.145-148

DOI: 10.1109/imtc.1994.352103

[5] H. Simon, Xiaobo Li: Detection of signals in chaos, Proceedings of the IEEE, Vol.83 (1995), pp.95-122

[6] A. Wolf, J. B. Swift, and H. L. Swinney: Determining Lyapunov exponents from a time series, Physica D, Vol.16 (1985), pp.285-317

DOI: 10.1016/0167-2789(85)90011-9