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Ultra-Short-Term Wind Power Combined Prediction Based on Complementary Ensemble Empirical Mode Decomposition, Whale Optimisation Algorithm, and Elman Network

Author

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  • Anfeng Zhu

    (Engineering Research Center of Hunan Province for the Mining and Utilization of Wind Turbines Operation Data, Hunan University of Science and Technology, Xiangtan 411201, China)

  • Qiancheng Zhao

    (Engineering Research Center of Hunan Province for the Mining and Utilization of Wind Turbines Operation Data, Hunan University of Science and Technology, Xiangtan 411201, China)

  • Xian Wang

    (Engineering Research Center of Hunan Province for the Mining and Utilization of Wind Turbines Operation Data, Hunan University of Science and Technology, Xiangtan 411201, China)

  • Ling Zhou

    (Engineering Research Center of Hunan Province for the Mining and Utilization of Wind Turbines Operation Data, Hunan University of Science and Technology, Xiangtan 411201, China)

Abstract

Accurate wind power forecasting helps relieve the regulation pressure of a power system, which is of great significance to the power system’s operation. However, achieving satisfactory results in wind power forecasting is highly challenging due to the random volatility characteristics of wind power sequences. This study proposes a novel ultra-short-term wind power combined prediction method based on complementary ensemble empirical mode decomposition, the whale optimization algorithm (WOA), and the Elman neural network model. The model can not only solve the phenomenon of easy modal mixing in decomposition but also avoid the problems of reconstruction error and low efficiency in the decomposition process. Furthermore, a new metaheuristic algorithm, WOA, was introduced to optimize the model and improve the accuracy of wind power prediction. Considering a wind farm as an example, several wind turbines were selected to simulate and analyse wind power by using the established prediction model, and the experimental results suggest that the proposed method has a higher prediction accuracy of ultra-short-term wind power than other prediction models.

Suggested Citation

  • Anfeng Zhu & Qiancheng Zhao & Xian Wang & Ling Zhou, 2022. "Ultra-Short-Term Wind Power Combined Prediction Based on Complementary Ensemble Empirical Mode Decomposition, Whale Optimisation Algorithm, and Elman Network," Energies, MDPI, vol. 15(9), pages 1-17, April.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:9:p:3055-:d:799248
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    References listed on IDEAS

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    Cited by:

    1. Hua Li & Zhen Wang & Binbin Shan & Lingling Li, 2022. "Research on Multi-Step Prediction of Short-Term Wind Power Based on Combination Model and Error Correction," Energies, MDPI, vol. 15(22), pages 1-21, November.
    2. Xiaomei Wu & Songjun Jiang & Chun Sing Lai & Zhuoli Zhao & Loi Lei Lai, 2022. "Short-Term Wind Power Prediction Based on Data Decomposition and Combined Deep Neural Network," Energies, MDPI, vol. 15(18), pages 1-16, September.
    3. G. Ponkumar & S. Jayaprakash & Karthick Kanagarathinam, 2023. "Advanced Machine Learning Techniques for Accurate Very-Short-Term Wind Power Forecasting in Wind Energy Systems Using Historical Data Analysis," Energies, MDPI, vol. 16(14), pages 1-24, July.
    4. Yiyang Sun & Xiangwen Wang & Junjie Yang, 2022. "Modified Particle Swarm Optimization with Attention-Based LSTM for Wind Power Prediction," Energies, MDPI, vol. 15(12), pages 1-17, June.
    5. Qiuhong Huang & Xiao Wang, 2022. "A Forecasting Model of Wind Power Based on IPSO–LSTM and Classified Fusion," Energies, MDPI, vol. 15(15), pages 1-19, July.

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