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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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    16. Navas, R Kaja Bantha & Prakash, S & Sasipraba, T, 2020. "Artificial Neural Network based computing model for wind speed prediction: A case study of Coimbatore, Tamil Nadu, India," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 542(C).
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