RESEARCH ON ULTRA-SHORT-TERM WIND POWER PREDICTION BASED ON CEEMD-GA-BiGRU-Attention

Wang Zitao; Liu Xinyi; Wang Yang; Liu Shanke; Yu Lijun

doi:10.19912/j.0254-0096.tynxb.2025-0761

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (9) : 795-804. DOI: 10.19912/j.0254-0096.tynxb.2025-0761

RESEARCH ON ULTRA-SHORT-TERM WIND POWER PREDICTION BASED ON CEEMD-GA-BiGRU-Attention

Wang Zitao¹, Liu Xinyi², Wang Yang¹, Liu Shanke¹, Yu Lijun¹

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Abstract

To enhance the accuracy of ultra-short-term wind power prediction and investigate the contribution of diverse features to power prediction models, this paper introduces a wind power ultra-short-term prediction model grounded in CEEMD-GA-BiGRU-Attention. The model employs the SHAP value analysis method to assess the contribution of various features. Initially, the power data is decomposed using the Complete Ensemble Empirical Mode Decomposition （CEEMD） method. Subsequently, the Bidirectional Gated Recurrent Unit （BiGRU） model integrated with an attention mechanism is optimized using the Genetic Algorithm （GA） to dynamically model different modal components, thereby constructing a combined prediction model. When compared to various other power prediction models, this proposed model demonstrates superiority in terms of RMSE, MAE, and R² evaluation metrics, with a notable 10.08% improvement in R² compared to the BiGRU model, and a reduction of 40.15% and 42.38% in RMSE and MAE, respectively. Finally, through the application of the SHAP value analysis method, it is revealed that historical actual power and weather forecast wind speed are the features that significantly contribute to the model. Historical actual power dominates in the initial stages of ultra-short-term prediction, but its importance gradually diminishes over time, whereas the importance of weather forecast wind speed exhibits a stable upward trend in the middle to later stages.

Key words

wind power prediction / attention mechanism / bidirectional gated recurrent unit / interpretability

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Wang Zitao, Liu Xinyi, Wang Yang, Liu Shanke, Yu Lijun. RESEARCH ON ULTRA-SHORT-TERM WIND POWER PREDICTION BASED ON CEEMD-GA-BiGRU-Attention[J]. Acta Energiae Solaris Sinica. 2026, 47(9): 795-804 https://doi.org/10.19912/j.0254-0096.tynxb.2025-0761

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