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Wind Power Forecasting Using Hybrid ARIMA-ANN Technique

  • Pavan Kumar Singh
  • Nitin SinghEmail author
  • Richa Negi
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 904)

Abstract

The wind power forecasting along with the prior knowledge of wind speed has become very important for the efficient functioning of wind power generation and effective management of risk and revenue. Several single approach models are there for forecasting of wind power, i.e., ARIMA, support vector machine (SVM), artificial neural networks (ANN), extreme learning machine (ELM), etc., but hybridization of these models is considered as an effective alternative for forecasting. In the proposed work, the hybridized model combining ARIMA and artificial neural network (ANN) is presented in order to provide a better prediction of wind power. The wind speed data of Denmark is used for evaluation of the proposed model. From the result obtained, it becomes evident that the hybridization of ARIMA and ANN is better in forecasting the wind power as compared to the two models working separately for wind power forecasting.

Keywords

Wind energy forecasting Wind speed forecasting Smart grid ARIMA ANN Hybrid model Renewable energy 

References

  1. 1.
    Ye, R., Suganthan, P.N., Srikanth, N., Sarkar, S.: A hybrid ARIMA-DENFIS method for wind speed forecasting. IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1–6 (2013).  https://doi.org/10.1109/FUZZ-IEEE.2013.6622503
  2. 2.
    Soman, S.S., Zareipour, H., Malik, O., Mandal, P.: A review of wind power and wind speed forecasting methods with different time horizons, North American Power Symposium. pp. 1–8 (2010).  https://doi.org/10.1109/NAPS.2010.5619586
  3. 3.
    Radziukynas, V., Klementavičius, A.: Short-term wind speed forecasting with ARIMA model. IEEE. 55th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON). pp. 145–149 (2014).  https://doi.org/10.1109/RTUCON.2014.6998223
  4. 4.
    Hill, D.C., McMillan, D., Bell, K.R.W., Infield, D.: Application of auto-regressive models to U.K. wind speed data for power system impact studies. IEEE Trans. Sustain. Energy 3(1), 134–141 (2012).  https://doi.org/10.1109/TSTE.2011.2163324CrossRefGoogle Scholar
  5. 5.
    Eshel, G.: The Yule Walker equations for the AR coefficients. University of South Carolina, 215. Tech. Rep. 1–8 (2010). [Online]. Available: http://www.stat.sc.edu/~vesselin/STAT520_YW.pdf
  6. 6.
    Eldali, F.A., Hansen, T.M., Suryanarayanan, S., Chong, E.K.: Employing ARIMA models to improve wind power forecasts: a case study in ERCOT, IEEE. North American Power Symposium (NAPS), pp.1–6 (2016).  https://doi.org/10.1109/NAPS.2016.7747861
  7. 7.
    Pai, P.-F., Lin, C.-S.: “A hybrid arima and support vector machines model in stock price forecasting”. Omega 33(6), 497–505 (2005)CrossRefGoogle Scholar
  8. 8.
    Min, Y., Bin, W., Liang-Li, Z., Xi, C.: “Wind speed forecasting based on EEMD and ARIMA”. IEEE, pp. 1299–1302 (2015).  https://doi.org/10.1016/j.omega.2004.07.024
  9. 9.
    Carolin, M., Fernandez, M.E.: “Analysis of wind power generation and prediction using ANN: a case study”. Renew. Energy 33, 986–992 (2008)  https://doi.org/10.1016/j.renene.2007.06.013
  10. 10.
    Catalão, J.P.S., Pousinho, H.M.I., Mendes, V.M.F.: “Short-term wind power forecasting in Portugal by neural networks and wavelet transform”. Renewable Energy 36, 1245–1251 (2011)  https://doi.org/10.1016/j.renene.2010.09.016
  11. 11.
    Catalao, J.P.S., Pousinho, H.M.I., Mendes, V.M.F.: “An artificial neural network approach for short-term wind power forecasting in Portugal”. 15th International Conference on Intelligent System Applications to Power Systems, pp. 1–5 (2009)  https://doi.org/10.1109/isap.2009.5352853
  12. 12.
    Kasabov, N.K., Song, Q.: “DENFIS: dynamic evolving neural-fuzzy inference system and its application for time-series prediction”. IEEE Trans. Fuzzy Syst. 10 (2), 144–154 (2002)  https://doi.org/10.1109/91.995117CrossRefGoogle Scholar
  13. 13.
    Kariniotakis, G.N., Stavrakakis, G.S., Nogaret, E.F.: “Wind power forecasting using advanced neural networks models”. IEEE Trans. Energy. Conver. 11(4), 762–767 (1996)  https://doi.org/10.1109/60.556376
  14. 14.
    Cadenas, E., Rivera, W.: “Wind speed forecasting in three different regions of Mexico, using a hybrid ARIMA-ANN model”. Renew. Energy 35(12), 2732–2738 (2010)  https://doi.org/10.1016/j.renene.2010.04.022CrossRefGoogle Scholar
  15. 15.
    Chang, G.W., Lu, H.J., Hsu, L.Y., Chen, Y.Y.: “A hybrid model for forecasting wind speed and wind power generation”. IEEE Power and Energy Society General Meeting (PESGM), pp. 1–5 (2016)  https://doi.org/10.1109/pesgm.2016.7742039

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Electrical EngineeringMNNIT AllahabadAllahabadIndia

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