Development of a Power Output Forecasting Tool for Wind Farms Based in Principal Components and Artificial Neural Networks

  • P. del Saz-OrozcoEmail author
  • J. Fernández de Cañete
  • R. Alba
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9094)


The main objective of the study here presented consists in developing a mathematical forecasting model of the available wind power output for an eight-hour horizon in wind farms that may be affected by inclement meteorological environments where the surface of the wind turbine blades can suffer of ice accumulation. These events may depend on several factors as air temperature, relative humidity, barometric pressure or wind speed, among others. In this way a precise model depending on the referred variables will allow predicting with higher accuracy the available power at the plant when the referred events may occur. A model based in neural networks for the prediction of the available power output of an experimental wind farm has been developed and tested using real data. The proposed model outperforms other professional commercial models.


Wind power forecasting Artificial neural networks Principal components analysis 


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  1. 1.
    Gasch, R., Twele, J. (ed.): Wind Power Plants. Fundamentals, Design, Construction and Operation, 2nd edn. Springer-Verlag (2012)Google Scholar
  2. 2.
    de Sistiernes, F.: Quantifying the Combined impact of Wind and Solar Power Penetration in the Optimal Generation Mix and Thermal Power Plant Cycling. Young Energy economists and Engineers (2011)Google Scholar
  3. 3.
    Giebel, G., Brownsword, R., Kariniotakis, G., Denhard, M., Draxl, C.: The State-of-the-art in Short-Term prediction of Wind Power. In: Deliverable D-1.2 project, 6th Framework Program (2011)Google Scholar
  4. 4.
    Foley, A., Leahy, P., Marvuglia, A., McKeogh, E.: Current methods and advances in forecasting of wind power generation. Renewable Energy 37, 1–8 (2012)CrossRefGoogle Scholar
  5. 5.
    Magnusson, M., Wern L.: Wind energy predictions using CFD and HIRLAM forecast. In: Proceedings of the European wind energy conference EWEC2001. Copenhagen, Denmark (2001)Google Scholar
  6. 6.
    Langberg, L.: Short term prediction of power production from wind Farms. Journal of Wind Engineering and Industrial Aerodynamics 80, 207–220 (1999)CrossRefGoogle Scholar
  7. 7.
    Langberg, L.: Short term prediction of local wind conditions. Journal of Wind Engineering and Industrial Aerodynamics 89(3/4), 235–245 (2001)CrossRefGoogle Scholar
  8. 8.
    Pinson, P., Madsen, H.: Adaptive modeling and forecasting of offshore wind power fluctuation with Markov-switching autoregressive models. Journal of forecasting 31(4), 281–313 (2012)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Karki, R., Hu, P., Billinton, R.: A Simplified wind power generation model for reliability Evaluation. IEEE transaction on Energy conversion 21(2), 533–540 (2006)CrossRefGoogle Scholar
  10. 10.
    Erdem, E., Shi, J.: ARIMA based approaches for forecasting the tuple of wind speed and direction. Applied Energy 88(4), 1405–1414 (2011)CrossRefGoogle Scholar
  11. 11.
    Chen, P., Pedersen, T., Bak-Jensen, B., Chen, Z.: ARIMA-based time series model of stochastic wind power generation. IEEE transactions on power systems 25(2), 667–676 (2010)CrossRefGoogle Scholar
  12. 12.
    Cassola, F., Burlando, M.: Wind Speed and wind energy forecast through Kalman filtering of Numerical Weather prediction model output. Applied Energy 99, 154–166 (2012)CrossRefGoogle Scholar
  13. 13.
    Methaprayoon, K., Yingvivatanapong, C., Lee, W., Liao, J.: An Integration of ANN Wind Power Estimation Into Unit Commitment Considering the Forecasting Uncertainty. IEEE Transactions on Industry Applications 43(6), 1441–1448 (2007)CrossRefGoogle Scholar
  14. 14.
    Pinson, P., Siebert, N., Kariniotakis, G.: Forecasting of regional wind generation by a dynamic fuzzy-neural networks based upscaling approach. In: European Wind Energy Conference and Exhibition, EWEC 2003 (2003)Google Scholar
  15. 15.
    Fugon, L. Juban, J.; Kariniotakis, G.: Data Mining for wind power forecasting. In: European Wind Energy confenernce and Exhibition, EWEC 2008 (2008)Google Scholar
  16. 16.
    Kusiak, A., Li, W.: Short-term prediction of wind power with a clustering approach. Renewable Energy 35, 2362–2369 (2010)CrossRefGoogle Scholar
  17. 17.
    Rahmani, R., Yusof, R., Seyedmahmoudian, M., Mekhilef, S.: Hybrid technique of ant colony and particle swarm optimization for short term wind energy forecasting. Journal of Wind Engineering and Industrial Aerodynamics. 123(part A), 163–170 (2013)CrossRefGoogle Scholar
  18. 18.
    Homola, M.: Impacts and causes of icing on wind turbines. In: Interreg III project Wind Energy in the BSR: Impacts and Causes of Icing on Wind Turbines, European Union Project. Narvik University College (2005)Google Scholar
  19. 19.
    Febrel, A.: Estudio de Prestación de Servicios Complementarios con parques Eólicos. Bachelor Thesis (Industrial Engineering), Comillas Pontifical University, Madrid (2006)Google Scholar
  20. 20.
    Borggrefe, F., Neuhoff, K.: Balancing and Intraday Market Design: Options for Wind Integration. Climate Policy Initiative working paper (2011)Google Scholar
  21. 21.
    Tammeling, B., Cavaliere, M., Holttinen, H., Morgan, G., Seifer, H., Säntti, K.: Publishable report. Wind Energy production in cold Climate. Framework of the Non-Nuclear Energy programme (1998)Google Scholar
  22. 22.
    Jang, R., Sung, C., Mizutani, E.: Neuro-Fuzzy and Soft Computing: A computational Approach to Learning and Machine Intelligence. Prentice Hall (1997)Google Scholar
  23. 23.
    Jolliffe, I.T.: Principal Component Analysis 2nd edn. Springer Series in Statistics (2002)Google Scholar
  24. 24.
    Zugno, M., Jónsson, T., Pinson, P.: Trading wind energy on the basis of probabilistic forecasts both of wind generation and of market quantities. Wind Energy 16(6), 909–926 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • P. del Saz-Orozco
    • 1
    Email author
  • J. Fernández de Cañete
    • 1
  • R. Alba
    • 2
  1. 1.System Engineering and Automation DepartmentUniversity of MalagaMalagaSpain
  2. 2.Gas Natural FenosaMadridSpain

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