SHORT-TERM WIND POWER PREDICTION BY CPO-VMD-FE REFINED POWER SERIES AND NRBO OPTIMIZED LEARNING MODEL

Huang Zhao; Yang Yuanwen; Wang Xin; Guo Zhiwei; Zhang Liu

doi:10.19912/j.0254-0096.tynxb.2024-2345

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (5) : 268-277. DOI: 10.19912/j.0254-0096.tynxb.2024-2345

SHORT-TERM WIND POWER PREDICTION BY CPO-VMD-FE REFINED POWER SERIES AND NRBO OPTIMIZED LEARNING MODEL

Huang Zhao¹, Yang Yuanwen^1,2, Wang Xin², Guo Zhiwei¹, Zhang Liu¹

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Abstract

To address the insufficient accuracy of wind power forecasting caused by missing meteorological data, a hybrid model combining crested porcupine optimizer-variational modal decomposition （CPO-VMD） and Newton-Raphson based optimizer-long short term memory-attention mechanism （NRBO-LSTM-Attention） is proposed. First, the CPO algorithm adaptively optimizes VMD parameters to decompose the raw power series, and similar components are reconstructed based on fuzzy entropy （FE） to reduce data complexity. Then, the NRBO algorithm is used to optimize the parameters of the LSTM-Attention network, enhancing the dynamic capture capability of temporal features. Simulation results show that the model achieves ideal prediction accuracy in under missing meteorological data, providing reliable technical support for wind power dispatch in complex environments.

Key words

variational modal decomposition / wind power / neural network / Newton-Raphson-based optimizer / attention mechanism

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Huang Zhao, Yang Yuanwen, Wang Xin, Guo Zhiwei, Zhang Liu. SHORT-TERM WIND POWER PREDICTION BY CPO-VMD-FE REFINED POWER SERIES AND NRBO OPTIMIZED LEARNING MODEL[J]. Acta Energiae Solaris Sinica. 2026, 47(5): 268-277 https://doi.org/10.19912/j.0254-0096.tynxb.2024-2345

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