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A Hybrid Method for Short-Term Wind Speed Forecasting

Author

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  • Jinliang Zhang

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China
    Center for Energy and Environmental Policy Research, Beijing Institute of Technology, Beijing 100181, China)

  • YiMing Wei

    (Center for Energy and Environmental Policy Research, Beijing Institute of Technology, Beijing 100181, China
    School of Management and Economics, Beijing Institute of Technology, Beijing 102206, China)

  • Zhong-fu Tan

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Wang Ke

    (Center for Energy and Environmental Policy Research, Beijing Institute of Technology, Beijing 100181, China
    School of Management and Economics, Beijing Institute of Technology, Beijing 102206, China)

  • Wei Tian

    (School of Management and Economics, Illinois Institute of Technology, Chicago, IL 60616, USA)

Abstract

The accuracy of short-term wind speed prediction is very important for wind power generation. In this paper, a hybrid method combining ensemble empirical mode decomposition (EEMD), adaptive neural network based fuzzy inference system (ANFIS) and seasonal auto-regression integrated moving average (SARIMA) is presented for short-term wind speed forecasting. The original wind speed series is decomposed into both periodic and nonlinear series. Then, the ANFIS model is used to catch the nonlinear series and the SARIMA model is applied for the periodic series. Numerical testing results based on two wind sites in South Dakota show the efficiency of this hybrid method.

Suggested Citation

  • Jinliang Zhang & YiMing Wei & Zhong-fu Tan & Wang Ke & Wei Tian, 2017. "A Hybrid Method for Short-Term Wind Speed Forecasting," Sustainability, MDPI, vol. 9(4), pages 1-10, April.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:4:p:596-:d:95633
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    References listed on IDEAS

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    1. Palm, F. & Zellner, A., 1991. "To combine or not to combine? issues of combining forecasts," LIDAM Discussion Papers CORE 1991022, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    2. Jung, Jaesung & Broadwater, Robert P., 2014. "Current status and future advances for wind speed and power forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 762-777.
    3. Liu, Heping & Shi, Jing & Erdem, Ergin, 2010. "Prediction of wind speed time series using modified Taylor Kriging method," Energy, Elsevier, vol. 35(12), pages 4870-4879.
    4. Guo, Zhenhai & Zhao, Jing & Zhang, Wenyu & Wang, Jianzhou, 2011. "A corrected hybrid approach for wind speed prediction in Hexi Corridor of China," Energy, Elsevier, vol. 36(3), pages 1668-1679.
    5. Beccali, M. & Cirrincione, G. & Marvuglia, A. & Serporta, C., 2010. "Estimation of wind velocity over a complex terrain using the Generalized Mapping Regressor," Applied Energy, Elsevier, vol. 87(3), pages 884-893, March.
    6. Kavasseri, Rajesh G. & Seetharaman, Krithika, 2009. "Day-ahead wind speed forecasting using f-ARIMA models," Renewable Energy, Elsevier, vol. 34(5), pages 1388-1393.
    7. Thiaw, L. & Sow, G. & Fall, S.S. & Kasse, M. & Sylla, E. & Thioye, S., 2010. "A neural network based approach for wind resource and wind generators production assessment," Applied Energy, Elsevier, vol. 87(5), pages 1744-1748, May.
    8. 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.
    9. Chen, Tai-Liang & Cheng, Ching-Hsue & Teoh, Hia-Jong, 2008. "High-order fuzzy time-series based on multi-period adaptation model for forecasting stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(4), pages 876-888.
    10. Carolin Mabel, M. & Fernandez, E., 2008. "Analysis of wind power generation and prediction using ANN: A case study," Renewable Energy, Elsevier, vol. 33(5), pages 986-992.
    11. Cadenas, Erasmo & Rivera, Wilfrido, 2009. "Short term wind speed forecasting in La Venta, Oaxaca, México, using artificial neural networks," Renewable Energy, Elsevier, vol. 34(1), pages 274-278.
    12. Abdel-Aal, R.E. & Elhadidy, M.A. & Shaahid, S.M., 2009. "Modeling and forecasting the mean hourly wind speed time series using GMDH-based abductive networks," Renewable Energy, Elsevier, vol. 34(7), pages 1686-1699.
    13. 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.
    14. 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.
    15. 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.
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