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Short-Term Wind Speed Forecasting Based on Signal Decomposing Algorithm and Hybrid Linear/Nonlinear Models

Author

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  • Qinkai Han

    (The State Key Laboratory of Tribology, Tsinghua University, Beijing 100084, China)

  • Hao Wu

    (School of Mathematical Sciences, Capital Normal University, Beijing 100048, China)

  • Tao Hu

    (School of Mathematical Sciences, Capital Normal University, Beijing 100048, China)

  • Fulei Chu

    (The State Key Laboratory of Tribology, Tsinghua University, Beijing 100084, China)

Abstract

Accurate wind speed forecasting is a significant factor in grid load management and system operation. The aim of this study is to propose a framework for more precise short-term wind speed forecasting based on empirical mode decomposition (EMD) and hybrid linear/nonlinear models. Original wind speed series is decomposed into a finite number of intrinsic mode functions (IMFs) and residuals by using the EMD. Several popular linear and nonlinear models, including autoregressive integrated moving average (ARIMA), support vector machine (SVM), random forest (RF), artificial neural network with back propagation (BP), extreme learning machines (ELM) and convolutional neural network (CNN), are utilized to study IMFs and residuals, respectively. An ensemble forecast for the original wind speed series is then obtained. Various experiments were conducted on real wind speed series at four wind sites in China. The performance and robustness of various hybrid linear/nonlinear models at two time intervals (10 min and 1 h) are compared comprehensively. It is shown that the EMD based hybrid linear/nonlinear models have better accuracy and more robust performance than the single models with/without EMD. Among the five hybrid models, EMD-ARIMA-RF has the best accuracy on the whole for 10 min data, and the mean absolute percentage error (MAPE) is less than 0.04. However, for the 1 h data, no model can always perform well on the four datasets, and the MAPE is around 0.15.

Suggested Citation

  • Qinkai Han & Hao Wu & Tao Hu & Fulei Chu, 2018. "Short-Term Wind Speed Forecasting Based on Signal Decomposing Algorithm and Hybrid Linear/Nonlinear Models," Energies, MDPI, vol. 11(11), pages 1-23, November.
  • Handle: RePEc:gam:jeners:v:11:y:2018:i:11:p:2976-:d:179736
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    References listed on IDEAS

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    7. Musaed Alhussein & Syed Irtaza Haider & Khursheed Aurangzeb, 2019. "Microgrid-Level Energy Management Approach Based on Short-Term Forecasting of Wind Speed and Solar Irradiance," Energies, MDPI, vol. 12(8), pages 1-27, April.

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