ULTRA-SHORT-TERM WIND POWER FORECASTING BASED ON FLUCTUATION CONTINUATION SCENARIO IDENTIFICATION

Liu Xiaoyan; Zhen Zhao; Wang Fei; Huang Yuehui; Chang Xiqiang; Mi Zengqiang

doi:10.19912/j.0254-0096.tynxb.2025-0585

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (8) : 724-736. DOI: 10.19912/j.0254-0096.tynxb.2025-0585

ULTRA-SHORT-TERM WIND POWER FORECASTING BASED ON FLUCTUATION CONTINUATION SCENARIO IDENTIFICATION

Liu Xiaoyan^1,2, Zhen Zhao^1,2, Wang Fei^1,2, Huang Yuehui³, Chang Xiqiang⁴, Mi Zengqiang^1,2

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Abstract

Addressing the limitations of existing wind power forecasting methods, which arise from insufficient extraction of wind power fluctuation information and inadequate analysis of evolutionary patterns, this paper proposes an ultra-short-term wind power forecasting method based on fluctuation continuation scenario identification. First, the coupling mechanism of wind power fluctuations under multiple turbulent processes is investigated, and historical power data are dynamically decoupled into combined nonlinear and linear fluctuation components. The concept of fluctuation extensibility is then introduced to quantify the future persistence scale of wind power fluctuations, which is derived from nonlinear and linear decoupling parameters, based on which fluctuation-extensibility scenarios are defined. Furthermore, a sparse neural network （SNN） tailored for high-dimensional sparse features is developed to identify fluctuation-continuation scenarios of historical wind power data and perform scenario-based ultra-short-term power forecasting. Finally, experiments are conducted on measured wind speed and power data from three wind farms. The results show that, compared with the benchmark models, the proposed method reduces the root mean square error （RMSE）, mean absolute error （MAE）, and mean absolute percentage error （MAPE） by at least 1.46%, 2.44%, and 14.67%, respectively, which verifies its superior predictive accuracy and stability.

Key words

wind farm / neural networks / power generation / feature extraction / wind power forecasting

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Liu Xiaoyan, Zhen Zhao, Wang Fei, Huang Yuehui, Chang Xiqiang, Mi Zengqiang. ULTRA-SHORT-TERM WIND POWER FORECASTING BASED ON FLUCTUATION CONTINUATION SCENARIO IDENTIFICATION[J]. Acta Energiae Solaris Sinica. 2026, 47(8): 724-736 https://doi.org/10.19912/j.0254-0096.tynxb.2025-0585

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