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Short-term wind speed prediction using an unscented Kalman filter based state-space support vector regression approach

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  • Chen, Kuilin
  • Yu, Jie

Abstract

Accurate wind speed forecasting is becoming increasingly important to improve and optimize renewable wind power generation. Particularly, reliable short-term wind speed prediction can enable model predictive control of wind turbines and real-time optimization of wind farm operation. However, this task remains challenging due to the strong stochastic nature and dynamic uncertainty of wind speed. In this study, unscented Kalman filter (UKF) is integrated with support vector regression (SVR) based state-space model in order to precisely update the short-term estimation of wind speed sequence. In the proposed SVR–UKF approach, support vector regression is first employed to formulate a nonlinear state-space model and then unscented Kalman filter is adopted to perform dynamic state estimation recursively on wind sequence with stochastic uncertainty. The novel SVR–UKF method is compared with artificial neural networks (ANNs), SVR, autoregressive (AR) and autoregressive integrated with Kalman filter (AR-Kalman) approaches for predicting short-term wind speed sequences collected from three sites in Massachusetts, USA. The forecasting results indicate that the proposed method has much better performance in both one-step-ahead and multi-step-ahead wind speed predictions than the other approaches across all the locations.

Suggested Citation

  • Chen, Kuilin & Yu, Jie, 2014. "Short-term wind speed prediction using an unscented Kalman filter based state-space support vector regression approach," Applied Energy, Elsevier, vol. 113(C), pages 690-705.
    • Handle: RePEc:eee:appene:v:113:y:2014:i:c:p:690-705
    DOI: 10.1016/j.apenergy.2013.08.025
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    1. Sfetsos, A., 2002. "A novel approach for the forecasting of mean hourly wind speed time series," Renewable Energy, Elsevier, vol. 27(2), pages 163-174.
    2. Mohandes, M.A. & Halawani, T.O. & Rehman, S. & Hussain, Ahmed A., 2004. "Support vector machines for wind speed prediction," Renewable Energy, Elsevier, vol. 29(6), pages 939-947.
    3. Cao, Qing & Ewing, Bradley T. & Thompson, Mark A., 2012. "Forecasting wind speed with recurrent neural networks," European Journal of Operational Research, Elsevier, vol. 221(1), pages 148-154.
    4. Xydis, G. & Koroneos, C. & Loizidou, M., 2009. "Exergy analysis in a wind speed prognostic model as a wind farm sitting selection tool: A case study in Southern Greece," Applied Energy, Elsevier, vol. 86(11), pages 2411-2420, November.
    5. Salcedo-Sanz, Sancho & Ángel M. Pérez-Bellido, & Ortiz-García, Emilio G. & Portilla-Figueras, Antonio & Prieto, Luis & Paredes, Daniel, 2009. "Hybridizing the fifth generation mesoscale model with artificial neural networks for short-term wind speed prediction," Renewable Energy, Elsevier, vol. 34(6), pages 1451-1457.
    6. De Giorgi, Maria Grazia & Ficarella, Antonio & Tarantino, Marco, 2011. "Error analysis of short term wind power prediction models," Applied Energy, Elsevier, vol. 88(4), pages 1298-1311, April.
    7. Kavasseri, Rajesh G. & Seetharaman, Krithika, 2009. "Day-ahead wind speed forecasting using f-ARIMA models," Renewable Energy, Elsevier, vol. 34(5), pages 1388-1393.
    8. Liu, Hui & Tian, Hong-Qi & Chen, Chao & Li, Yan-fei, 2010. "A hybrid statistical method to predict wind speed and wind power," Renewable Energy, Elsevier, vol. 35(8), pages 1857-1861.
    9. Fadare, D.A., 2010. "The application of artificial neural networks to mapping of wind speed profile for energy application in Nigeria," Applied Energy, Elsevier, vol. 87(3), pages 934-942, March.
    10. Ucar, Aynur & Balo, Figen, 2009. "Investigation of wind characteristics and assessment of wind-generation potentiality in Uludag-Bursa, Turkey," Applied Energy, Elsevier, vol. 86(3), pages 333-339, March.
    11. Monfared, Mohammad & Rastegar, Hasan & Kojabadi, Hossein Madadi, 2009. "A new strategy for wind speed forecasting using artificial intelligent methods," Renewable Energy, Elsevier, vol. 34(3), pages 845-848.
    12. Cassola, Federico & Burlando, Massimiliano, 2012. "Wind speed and wind energy forecast through Kalman filtering of Numerical Weather Prediction model output," Applied Energy, Elsevier, vol. 99(C), pages 154-166.
    13. Erdem, Ergin & Shi, Jing, 2011. "ARMA based approaches for forecasting the tuple of wind speed and direction," Applied Energy, Elsevier, vol. 88(4), pages 1405-1414, April.
    14. Morales, J.M. & Mínguez, R. & Conejo, A.J., 2010. "A methodology to generate statistically dependent wind speed scenarios," Applied Energy, Elsevier, vol. 87(3), pages 843-855, March.
    15. Costa, Alexandre & Crespo, Antonio & Navarro, Jorge & Lizcano, Gil & Madsen, Henrik & Feitosa, Everaldo, 2008. "A review on the young history of the wind power short-term prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 12(6), pages 1725-1744, August.
    16. Li, Gong & Shi, Jing, 2010. "On comparing three artificial neural networks for wind speed forecasting," Applied Energy, Elsevier, vol. 87(7), pages 2313-2320, July.
    17. Celik, Ali N. & Kolhe, Mohan, 2013. "Generalized feed-forward based method for wind energy prediction," Applied Energy, Elsevier, vol. 101(C), pages 582-588.
    18. Pinson, P. & Nielsen, H.Aa. & Madsen, H. & Kariniotakis, G., 2009. "Skill forecasting from ensemble predictions of wind power," Applied Energy, Elsevier, vol. 86(7-8), pages 1326-1334, July.
    19. Riahy, G.H. & Abedi, M., 2008. "Short term wind speed forecasting for wind turbine applications using linear prediction method," Renewable Energy, Elsevier, vol. 33(1), pages 35-41.
    20. Bilgili, Mehmet & Sahin, Besir & Yasar, Abdulkadir, 2007. "Application of artificial neural networks for the wind speed prediction of target station using reference stations data," Renewable Energy, Elsevier, vol. 32(14), pages 2350-2360.
    21. Jursa, René & Rohrig, Kurt, 2008. "Short-term wind power forecasting using evolutionary algorithms for the automated specification of artificial intelligence models," International Journal of Forecasting, Elsevier, vol. 24(4), pages 694-709.
    22. Liu, Hui & Tian, Hong-qi & Li, Yan-fei, 2012. "Comparison of two new ARIMA-ANN and ARIMA-Kalman hybrid methods for wind speed prediction," Applied Energy, Elsevier, vol. 98(C), pages 415-424.
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