Comparison Between Neuronal Networks and ANFIS for Wind Speed-Energy Forecasting

  • Helbert EspitiaEmail author
  • Guzmán Díaz
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 657)


The generation distributed systems are a good alternative for the reasonable use of energy. Moreover, neuronal networks are an appropriate option for modeling and control of nonlinear complex systems. The eolian energy has shown to be an alternative for electric power generation, even though it also presents limitations for proper management due to associated variations to weather conditions which affect wind speed. In this paper, considering the characteristics present in wind power, neuronal and neuro-fuzzy systems are suggested for the prediction of wind velocity associated whit wind power. The results show an adequate performance of neuro-fuzzy systems for forecasting of wind speed.


Forecasting Fuzzy systems Neuro-fuzzy Neuronal networks Wind power 


  1. 1.
    Dugan, R., McDermott, T., Ball, G.: Planning for distributed generation. IEEE Industry Application Magazine (2001)Google Scholar
  2. 2.
    Tong, J., Yu, T.: Nonlinear PID control design for improving stability of micro-turbine systems. In: Third International Conference on Electric Utility Deregulation and Restructuring and Power Technologies DRPT, pp. 2515–2518 (2008)Google Scholar
  3. 3.
    Mathew, S.: Wind Energy, Fundamentals, Resource Analysis and Economics. Springer, Heidelberg (2006)Google Scholar
  4. 4.
    Heier, S., Waddington, R.: Grid Integration of Wind Energy Conversion Systems, 2nd edn. Wiley, Hoboken (2006)Google Scholar
  5. 5.
    Khatamianfar, A., Khalid, M., Savkin, A., Agelidis, V.: Improving wind farm dispatch in the Australian electricity market with battery energy storage using model predictive control. IEEE Trans. Sustain. Energ. 4(3), 745–755 (2013)CrossRefGoogle Scholar
  6. 6.
    Teleke, S., Baran, M., Huang, A., Bhattacharya, S., Anderson, L.: Control strategies for battery energy storage for wind farm dispatching. IEEE Trans. Energ. Convers. 24(3), 725–732 (2009)CrossRefGoogle Scholar
  7. 7.
    Wang, X., Vilathgamuwa, D., Choi, S.: Determination of battery storage capacity in energy buffer for wind farm. IEEE Trans. Energ. Convers. 23(3), 868–878 (2008)CrossRefGoogle Scholar
  8. 8.
    Holttinen, H., Hirvonen, R.: Power system requirements for wind power. In: Ackermann, T. (ed.) Wind Power in Power Systems, pp. 143–167. Wiley, New York (2005)Google Scholar
  9. 9.
    Han, C., Huang, A., Baran, M., Bhattacharya, S., Litzenberger, W., Anderson, L., Johnson, A., Edris, A.: STATCOM impact study on the integration of a large wind farm into a weak loop power system. IEEE Trans. Energ. Convers. 23(1), 226–233 (2008)CrossRefGoogle Scholar
  10. 10.
    Lundsager, P., Barring-Gould, E.: Isolated systems with wind power. In: Ackermann, T. (ed.) Wind Power in Power Systems, pp. 299–329. Wiley, New York (2005)Google Scholar
  11. 11.
    Abdullah, M., Muttaqi, K., Sutanto, D., Agalgaonkar, A.: An effective power dispatch control strategy to improve generation schedulability and supply reliability of a wind farm using a battery energy storage system. IEEE Trans. Sustain. Energ. 6(3), 1093–1102 (2015)CrossRefGoogle Scholar
  12. 12.
    Borowy, B., Salameh, Z.: Dynamic response to a stand-alone wind energy conversion system with battery energy storage to a wind gust. IEEE Trans. Energ. Convers. 12, 73–78 (1997)CrossRefGoogle Scholar
  13. 13.
    Huang, C.: Modified neural network for dynamic control and operation of a hybrid generation systems. J. Appl. Res. Technol. 12(6) (2014)Google Scholar
  14. 14.
    Dowell, J., Pinson, P.: Very-short-term probabilistic wind power forecasts by sparse vector autoregression. IEEE Trans. Smart Grid 7(2), 763–770 (2015)Google Scholar
  15. 15.
    Hyndman, R., Athanasopoulos, G.: Forecasting: principles and practice. OTexts, Melbourne (2014)Google Scholar
  16. 16.
    Huarng, K., Yu, T.: The application of neural networks to forecast fuzzy time series. Physica A. Stat. Mech. Appl. 363(2), 481–491 (2006)CrossRefGoogle Scholar
  17. 17.
    Peng, H., Wu, S., Wei, C., Lee, S.: Time series forecasting with a neuro-fuzzy modeling scheme. Appl. Soft Comput. 32, 481–493 (2015)CrossRefGoogle Scholar
  18. 18.
    Singh, M., Singh, I., Verma, A.: Identification on non linear series-parallel model using neural network. MIT Int. J. Electr. Instrumen. Eng. 3(1), 21–23 (2013)Google Scholar
  19. 19.
    Torres, N., Hernandez, C., Pedraza, L.: Redes neuronales y predicción de tráfico. Revista Tecnura 15(29), 90–97 (2011)Google Scholar
  20. 20.
    Martinez, F., Gómez, D., Castiblanco, M.: Optimization of a neural architecture for the direct control of a Boost converter. Revista Tecnura 16(32), 41–49 (2012)CrossRefGoogle Scholar
  21. 21.
    Arrieta, J., Torres, J., Velásquez, H.: Predicciones de modelos econométricos y redes neuronales: el caso de la acción de SURAMINV. Semestre Económico 12(25), 95–109 (2009)Google Scholar
  22. 22.
    Pérez, F., Fernández, H.: Las redes neuronales y la evaluación del riesgo de crédito. Revista Ingenierpias Universidad de Medellín 6(10), 77–91 (2007)Google Scholar
  23. 23.
    Heaton, J.: Introduction to Neural Networks with Java. Heaton Research Inc. (2008)Google Scholar
  24. 24.
    Wang, L.: A Course on Fuzzy Systems and Control. Prentice Hall PTR, New Jersey (1997)Google Scholar
  25. 25.
    Del Brio, B., Sanz, A.: Redes Neuronales y Sistemas Difusos. Alfaomega, Segunda Edición (2006)Google Scholar
  26. 26.
    Babuska, R.: Fuzzy Modeling for Control. Kluwer Academic Publishers, Wiley, London, England (1998)Google Scholar
  27. 27.
    Jang, J., Sun, C., Mizutani, E.: Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. Matlab Curriculum Series (1990)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Universidad Distrital Francisco José de CaldasBogotáColombia
  2. 2.Universidad de OviedoOviedoSpain

Personalised recommendations