RESEARCH ON SHORT-TERM WIND POWER PREDICTION METHOD BASED ON SSA-VMD-LIESN

Yang Ningning; Wang Yixin; Wu Chaojun; Ma Zhirui

doi:10.19912/j.0254-0096.tynxb.2024-0027

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (5) : 440-447. DOI: 10.19912/j.0254-0096.tynxb.2024-0027

RESEARCH ON SHORT-TERM WIND POWER PREDICTION METHOD BASED ON SSA-VMD-LIESN

Yang Ningning¹, Wang Yixin¹, Wu Chaojun^2,3, Ma Zhirui¹

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Abstract

Accurate short-term wind power prediction is conducive to improving the power system regulation ability, enhancing the wind power consumption level, and providing a basis for reliable wind power optimization decisions. In order to improve the accuracy of short-term wind power prediction, the paper proposes a prediction model based on SSA-VMD-LIESN. Firstly, the optimal variational modal decomposition （VMD） parameters are solved by the sparrow search algorithm （SSA） to decompose the complex wind power historical data into modal components of different frequencies. Subsequently, the complexity is reflected by sample entropy calculation, and the components with similar characteristics are fused and reconstructed. Finally, the SSA-VMD-LIESN prediction model is composed by combining the Leaky-Integrator Echo State Network （LIESN） with good nonlinear prediction ability, and the prediction results are compared and analyzed with the traditional LIESN, the long short-term memory network （LSTM）, and the BP neural network. The results show that the model trains quickly and has good short-term wind power prediction capability.

Key words

wind power / forecasting / variational modal decomposition / sparrow search algorithm / leaky-integrator ESN

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Yang Ningning, Wang Yixin, Wu Chaojun, Ma Zhirui. RESEARCH ON SHORT-TERM WIND POWER PREDICTION METHOD BASED ON SSA-VMD-LIESN[J]. Acta Energiae Solaris Sinica. 2025, 46(5): 440-447 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0027

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