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Wind Speed Forecasting Based on FEEMD and LSSVM Optimized by the Bat Algorithm

Author

Listed:
  • Wei Sun

    (Department of Business Administration, North China Electric Power University, Baoding 071000, China
    These authors contributed equally to this work.)

  • Mohan Liu

    (Department of Business Administration, North China Electric Power University, Baoding 071000, China)

  • Yi Liang

    (Department of Business Administration, North China Electric Power University, Baoding 071000, China
    These authors contributed equally to this work.)

Abstract

Affected by various environmental factors, wind speed presents high fluctuation, nonlinear and non-stationary characteristics. To evaluate wind energy properly and efficiently, this paper proposes a modified fast ensemble empirical model decomposition (FEEMD)-bat algorithm (BA)-least support vector machines (LSSVM) (FEEMD-BA-LSSVM) model combined with input selected by deep quantitative analysis. The original wind speed series are first decomposed into a limited number of intrinsic mode functions (IMFs) with one residual series. Then a LSSVM is built to forecast these sub-series. In order to select input from environment variables, Cointegration and Granger causality tests are proposed to check the influence of temperature with different leading lengths. Partial correlation is applied to analyze the inner relationships between the historical speeds thus to select the LSSVM input. The parameters in LSSVM are fine-tuned by BA to ensure the generalization of LSSVM. The forecasting results suggest the hybrid approach outperforms the compared models.

Suggested Citation

  • Wei Sun & Mohan Liu & Yi Liang, 2015. "Wind Speed Forecasting Based on FEEMD and LSSVM Optimized by the Bat Algorithm," Energies, MDPI, vol. 8(7), pages 1-23, June.
    • Handle: RePEc:gam:jeners:v:8:y:2015:i:7:p:6585-6607:d:51865
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    References listed on IDEAS

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    2. Zafar, Muhammad Hamza & Mansoor, Majad & Abou Houran, Mohamad & Khan, Noman Mujeeb & Khan, Kamran & Raza Moosavi, Syed Kumayl & Sanfilippo, Filippo, 2023. "Hybrid deep learning model for efficient state of charge estimation of Li-ion batteries in electric vehicles," Energy, Elsevier, vol. 282(C).
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    5. Mohammad Abdul Baseer & Anas Almunif & Ibrahim Alsaduni & Nazia Tazeen, 2023. "Electrical Power Generation Forecasting from Renewable Energy Systems Using Artificial Intelligence Techniques," Energies, MDPI, vol. 16(18), pages 1-21, September.
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    7. Erick López & Carlos Valle & Héctor Allende & Esteban Gil & Henrik Madsen, 2018. "Wind Power Forecasting Based on Echo State Networks and Long Short-Term Memory," Energies, MDPI, vol. 11(3), pages 1-22, February.
    8. Qunli Wu & Chenyang Peng, 2016. "A Least Squares Support Vector Machine Optimized by Cloud-Based Evolutionary Algorithm for Wind Power Generation Prediction," Energies, MDPI, vol. 9(8), pages 1-20, July.
    9. Yiqi Chu & Chengcai Li & Yefang Wang & Jing Li & Jian Li, 2016. "A Long-Term Wind Speed Ensemble Forecasting System with Weather Adapted Correction," Energies, MDPI, vol. 9(11), pages 1-20, October.
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    11. Shiyu Liu & Gengfeng Li & Haipeng Xie & Xifan Wang, 2017. "Correlation Characteristic Analysis for Wind Speed in Different Geographical Hierarchies," Energies, MDPI, vol. 10(2), pages 1-20, February.
    12. Abou Houran, Mohamad & Salman Bukhari, Syed M. & Zafar, Muhammad Hamza & Mansoor, Majad & Chen, Wenjie, 2023. "COA-CNN-LSTM: Coati optimization algorithm-based hybrid deep learning model for PV/wind power forecasting in smart grid applications," Applied Energy, Elsevier, vol. 349(C).
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