Browse > Article
http://dx.doi.org/10.5302/J.ICROS.2011.17.11.1173

Study on the Prediction of wind Power Generation Based on Artificial Neural Network  

Kim, Se-Yoon (Kunsan National University, School of Electronic & Information Engineering)
Kim, Sung-Ho (Kunsan National University, School of Electronic & Information Engineering)
Publication Information
Journal of Institute of Control, Robotics and Systems / v.17, no.11, 2011 , pp. 1173-1178 More about this Journal
Abstract
The power generated by wind turbines changes rapidly because of the continuous fluctuation of wind speed and direction. It is important for the power industry to have the capability to predict the changing wind power. In this paper, neural network based wind power prediction scheme which uses wind speed and direction is considered. In order to get a better prediction result, compression function which can be applied to the measurement data is introduced. Empirical data obtained from wind farm located in Kunsan is considered to verify the performance of the compression function.
Keywords
wind turbine; neural network; wind speed; wind direction; wind power prediction;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 S. S. Jo and Y. S. Son, "Time series analysis," Yulgok Publishers, 2009.
2 J. H. Lee, "Time series analysis and applications," Freedom Academy, 2007.
3 F. Lin, X. H. Yu, S. Gregor, and R. Irons, "Time series forecasting with neural networks," Complex Systems:Mechanism of Adaptation, pp. 245-252, 1994.
4 S. Li, D. C. Wunsch, E. O'Hair, and M. G. Giesselmann, "Neural network for wind power generation with compressing function," Neural Networks, International Conference on, pp. 115-120, 1997.
5 H. G. Beyer, D. Heinemann, H. Mellinghoff, K. Mönnich, and H. P. Waldl, "Forecast of regional power output of wind turbines," Proc. of the European Wind Energy Conference, Nice, France, March 1999.
6 G. Giebel, J. Badger, I. Martí Perez, P. Louka, G. Kallos, and A. M. Palomares, et al., "Short-term Forecasting Using Advanced Physical Modelling -," the Results of the Anemos Project, Results from mesoscale, microscale and CFD modeling. Proceedings of the European Wind Energy Conference, Athens, Greece, 27 February-2 March 2006.
7 S. Y. Kim and S. H. Kim, "Study on the prediction of wind power generation based on artificial neural network," Journal of Institute of Control, Robotics and Systems (in Korean), pp. 31-34, 2011.