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Wind Power Prediction Considering Nonlinear Atmospheric Disturbances

Author

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  • Yagang Zhang

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China
    Interdisciplinary Mathematics Institute, University of South Carolina, Columbia, SC 29208, USA)

  • Jingyun Yang

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China)

  • Kangcheng Wang

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China)

  • Zengping Wang

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China)

Abstract

This paper considers the effect of nonlinear atmospheric disturbances on wind power prediction. A Lorenz system is introduced as an atmospheric disturbance model. Three new improved wind forecasting models combined with a Lorenz comprehensive disturbance are put forward in this study. Firstly, we define the form of the Lorenz disturbance variable and the wind speed perturbation formula. Then, different artificial neural network models are used to verify the new idea and obtain better wind speed predictions. Finally we separately use the original and improved wind speed series to predict the related wind power. This proves that the corrected wind speed provides higher precision wind power predictions. This research presents a totally new direction in the wind prediction field and has profound theoretical research value and practical guiding significance.

Suggested Citation

  • Yagang Zhang & Jingyun Yang & Kangcheng Wang & Zengping Wang, 2015. "Wind Power Prediction Considering Nonlinear Atmospheric Disturbances," Energies, MDPI, vol. 8(1), pages 1-15, January.
    • Handle: RePEc:gam:jeners:v:8:y:2015:i:1:p:475-489:d:44640
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. Qunli Wu & Chenyang Peng, 2015. "Wind Power Grid Connected Capacity Prediction Using LSSVM Optimized by the Bat Algorithm," Energies, MDPI, vol. 8(12), pages 1-15, December.
    3. Yuansheng Huang & Lei Yang & Shijian Liu & Guangli Wang, 2019. "Multi-Step Wind Speed Forecasting Based On Ensemble Empirical Mode Decomposition, Long Short Term Memory Network and Error Correction Strategy," Energies, MDPI, vol. 12(10), pages 1-22, May.

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