Journal of Institute of Control, Robotics and Systems (제어로봇시스템학회논문지)

Institute of Control, Robotics and Systems (제어로봇시스템학회)
권호 표지
DOI QR코드

DOI QR Code

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

인공신경망 기반의 풍력발전기 발전량 예측에 관한 연구

  • Kim, Se-Yoon (Kunsan National University, School of Electronic & Information Engineering) ;
  • Kim, Sung-Ho (Kunsan National University, School of Electronic & Information Engineering)
  • 김세윤 (군산대학교 전자정보공학부) ;
  • 김성호 (군산대학교 전자정보공학부)
  • Received : 2011.07.09
  • Accepted : 2011.09.15
  • Published : 2011.11.01
Download PDF
⟨ Previous Next ⟩

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

References

  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.

Cited by

  1. A study on comparing short-term wind power prediction models in Gunsan wind farm vol.24, pp.3, 2013, https://doi.org/10.7465/jkdi.2013.24.3.585
  2. Design of short-term forecasting model of distributed generation power for wind power vol.12, pp.3, 2014, https://doi.org/10.14400/JDC.2014.12.3.211
  3. Analysis of prediction model for solar power generation vol.12, pp.3, 2014, https://doi.org/10.14400/JDC.2014.12.3.243
  4. Development of intelligent fault diagnostic system for mechanical element of wind power generator vol.24, pp.1, 2014, https://doi.org/10.5391/JKIIS.2014.24.1.078