WIND POWER INTERVAL PREDICTION BASED ON INTEGRATED DECOMPOSITION AND MUTIL-HEAD ATTENTION MECHANISM

Li Guomin; Zhang Jun; Zhao Chunyang; Li Gengyin; Zhang Yagang

doi:10.19912/j.0254-0096.tynxb.2024-2441

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (5) : 332-340. DOI: 10.19912/j.0254-0096.tynxb.2024-2441

WIND POWER INTERVAL PREDICTION BASED ON INTEGRATED DECOMPOSITION AND MUTIL-HEAD ATTENTION MECHANISM

Li Guomin, Zhang Jun, Zhao Chunyang, Li Gengyin, Zhang Yagang

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Abstract

Aiming at the limitations of traditional wind power prediction methods in dealing with complex signals, a new method for wind power prediction combining integrated decomposition and multi-model fusion is proposed. First, a Slime Chaos Levy （SCL） algorithm incorporating a hybrid strategy is proposed for optimizing the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise （ICEEMDAN） method, which is applied to the frequency decomposition of wind power data to decompose the signals into high, medium and low frequency components. Transformer, temporal convolutional network and bidirectional gated recurrent unit （TCN-BIGRU）, and online sequential extreme learning machine （OSELM） are applied to represent different frequency components in order to completely capture their temporal features and nonlinear patterns. Subsequently, to fully account for the contribution of various components to the wind power forecast, the prediction results of each frequency component are then weighted and fused using the multi-head attention mechanism. In order to obtain more robust wind power prediction results, the improved Markov chain Monte Carlo （IMCMC） method is utilized to predict the prediction results in intervals. The experimental findings demonstrate that the target method increases the R² by 14.2% and 7.15%, respectively, and the PICP is 95.78% and 95.93% at 95% confidence intervals, respectively, when compared to the typical single-model prediction method.

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wind power / optimization algorithms / decomposition methods / multi head attention mechanism / interval prediction

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Li Guomin, Zhang Jun, Zhao Chunyang, Li Gengyin, Zhang Yagang. WIND POWER INTERVAL PREDICTION BASED ON INTEGRATED DECOMPOSITION AND MUTIL-HEAD ATTENTION MECHANISM[J]. Acta Energiae Solaris Sinica. 2026, 47(5): 332-340 https://doi.org/10.19912/j.0254-0096.tynxb.2024-2441

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