SHORT-TERM WIND SPEED FORECASTING BASED ON CEEMD-SE-PSR-BP METHOD

Gao Shengyang; Li Fashe

doi:10.19912/j.0254-0096.tynxb.2023-2035

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (4) : 415-422. DOI: 10.19912/j.0254-0096.tynxb.2023-2035

SHORT-TERM WIND SPEED FORECASTING BASED ON CEEMD-SE-PSR-BP METHOD

Gao Shengyang^1,2, Li Fashe²

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Abstract

Accurate and reliable wind speed forecasting is vital for maintaining the stability of power systems. To improve prediction accuracy, this study introduces a novel short-term wind speed prediction model that integrates complementary ensemble empirical mode decomposition （CEEMD）, sample entropy （SE）, phase space reconstruction （PSR）, and a backpropagation neural network （BP）. Initially, CEEMD is employed to decompose the wind speed time series into multiple intrinsic mode functions （IMFs）, thereby simplifying the data structure. Following this, the SE of each IMF is calculated, and the wind speed sequence is reconstructed based on SE characteristics. Subsequently, the prediction results of each IMF undergo phase space reconstruction, yielding input-output samples for neural network prediction. Finally, the BP neural network is utilized to forecast each sample, and the predicted values are aggregated. The proposed model is evaluated using real-world data from a wind farm, and its performance is compared with other prediction methods. Experimental results demonstrate that this model significantly enhances wind speed prediction accuracy.

Key words

wind speed forecasting / sample entropy / complementary ensemble empirical mode decomposition / phase space reconstruction / neural networks / time series

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Gao Shengyang, Li Fashe. SHORT-TERM WIND SPEED FORECASTING BASED ON CEEMD-SE-PSR-BP METHOD[J]. Acta Energiae Solaris Sinica. 2025, 46(4): 415-422 https://doi.org/10.19912/j.0254-0096.tynxb.2023-2035

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