Advertisement

Probabilistic Wind Power Forecasting by Using Quantile Regression Analysis

  • Mehmet Baris Ozkan
  • Umut Guvengir
  • Dilek Küçük
  • Ali Unver Secen
  • Serkan Buhan
  • Turan Demirci
  • Abdullah Bestil
  • Ceyda Er
  • Pinar Karagoz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10691)

Abstract

Effective use of renewable energy sources, and in particular wind energy, is of paramount importance. Compared to other renewable energy sources, wind is so fluctuating that it must be integrated to the electricity grid in a planned way. Wind power forecast methods have an important role in this integration. These methods can be broadly classified as point wind power forecasting or probabilistic wind power forecasting methods. The point forecasting methods are more deterministic and they are concerned with the exact forecast for a particular time interval. These forecasts are very important especially for the Wind Power Plant (WPP) owners who attend the energy market with these forecasts from day-ahead. Probabilistic wind power forecasting is more crucial for the operational planning of the electricity grid by grid operators. In this methodology, the uncertainty in the wind power forecast for WPPs are presented within some confidence. This paper presents a probabilistic wind power forecasting method based on local quantile regression with Gaussian distribution. The method is applied to obtain probabilistic wind power forecasts, within the course of the Wind Power Monitoring and Forecast Center for Turkey (RİTM) project, which has been realized by TÜBİTAK MAM. Currently, 132 WPPs are included in the project and they are being monitored in real-time. In this paper, the results for 15 of these WPPs, which are selected from different regions of the country, are presented. The corresponding results are calculated for two different confidence intervals, namely 5–95 and 25–75 quantiles.

Keywords

Wind power forecasting Quantile regression Probabilistic forecast 

Notes

Acknowledgment

This work is conducted within the scope of RİTM project (with number 5122807), which is directed by Energy Institute of TÜBİTAK MAM. We would like thank all of the partners of the RİTM project especially to Renewable Energy General Directorate of Turkey (YEGM).

References

  1. 1.
    Hodge, B.-M., et al.: The value of improved short-term wind power forecasting. In: National Renewable Energy Laboratory (NREL), Golden, CO (2015)Google Scholar
  2. 2.
    Kou, P., Gao, F., Guan, X.: Stochastic predictive control of battery energy storage for wind farm dispatching: using probabilistic wind power forecasts. Renew. Energy 80, 286–300 (2015)CrossRefGoogle Scholar
  3. 3.
    Bremnes, J.B.: Probabilistic wind power forecasts using local quantile regression. Wind Energy 7(1), 47–54 (2004)CrossRefGoogle Scholar
  4. 4.
    Nielsen, H.A., Madsen, H., Nielsen, T.S.: Using quantile regression to extend an existing wind power forecasting system with probabilistic forecasts. Wind Energy 9(1–2), 95–108 (2006)CrossRefGoogle Scholar
  5. 5.
  6. 6.
    Juban, J., Siebert, N., Kariniotakis, G.N.: Probabilistic short-term wind power forecasting for the optimal management of wind generation. In: Power Tech, 2007 IEEE Lausanne (2007)Google Scholar
  7. 7.
    Sideratos, G., Hatziargyriou, N.D.: Probabilistic wind power forecasting using radial basis function neural networks. IEEE Trans. Power Syst. 27(4), 1788–1796 (2012)CrossRefGoogle Scholar
  8. 8.
    Carpinone, A., et al.: Very short-term probabilistic wind power forecasting based on Markov chain models. In: IEEE 11th International Conference on Probabilistic Methods Applied to Power Systems (PMAPS) (2010)Google Scholar
  9. 9.
    Yan, J., et al.: Hybrid probabilistic wind power forecasting using temporally local Gaussian process. IEEE Trans. Sustain. Energy 7(1), 87–95 (2016)CrossRefGoogle Scholar
  10. 10.
  11. 11.
    Ozkan, M.B., Karagoz, P.: A novel wind power forecast model: statistical hybrid wind power forecast technique (SHWIP). IEEE Trans. Ind. Inform. 11(2), 375–387 (2015)Google Scholar
  12. 12.
    Terciyanli, E., et al.: Enhanced nationwide wind-electric power monitoring and forecast system. IEEE Trans. Ind. Inf. 10(2), 1171–1184 (2014)CrossRefGoogle Scholar
  13. 13.
  14. 14.
  15. 15.
  16. 16.
    Brown, M.B., Forsythe, A.B.: Robust tests for the equality of variances. J. Am. Stat. Assoc. 69(346), 364–367 (1974)CrossRefMATHGoogle Scholar
  17. 17.
    Herrnstein, R.J., Murray, C.: Bell curve: Intelligence and class structure in American life. Simon and Schuster, New York (2010)Google Scholar
  18. 18.
  19. 19.
    Bremnes, J.B.: A comparison of a few statistical models for making quantile wind power forecasts. Wind Energy 9(1–2), 3–11 (2006)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Mehmet Baris Ozkan
    • 1
  • Umut Guvengir
    • 1
  • Dilek Küçük
    • 1
  • Ali Unver Secen
    • 1
  • Serkan Buhan
    • 1
  • Turan Demirci
    • 1
  • Abdullah Bestil
    • 1
  • Ceyda Er
    • 1
  • Pinar Karagoz
    • 2
  1. 1.TÜBİTAK MAM Energy InstituteAnkaraTurkey
  2. 2.Middle East Technical UniversityAnkaraTurkey

Personalised recommendations