RESEARCH ON ULTRA-SHORT-TERM WIND POWER FORECASTING MODELS BASED ON HYBRID DEEP LEARNING

Liu Shujun; Yu Xiaoqing; Du Xiaoze; Wu Jiangbo

doi:10.19912/j.0254-0096.tynxb.2025-0449

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (7) : 222-230. DOI: 10.19912/j.0254-0096.tynxb.2025-0449

RESEARCH ON ULTRA-SHORT-TERM WIND POWER FORECASTING MODELS BASED ON HYBRID DEEP LEARNING

Liu Shujun¹, Yu Xiaoqing¹, Du Xiaoze^1,2, Wu Jiangbo¹

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Abstract

This paper compares and analyzes the performance of single models, stacked hybrid deep learning networks, and parallel hybrid deep learning networks for ultrashortterm wind power forecasting. Hybrid forecasting models based on Long ShortTerm Memory （LSTM）, Temporal Convolutional Networks （TCN）, and Recurrent Neural Networks （RNN） are developed, including stacked architectures, hybrid parallel architectures, and dualparallel architectures. Experimental results show that parallelarchitecture deep learning networks for wind power forecasting significantly outperform stackedarchitecture models in terms of prediction accuracy and computational efficiency. Under the same hyperparameter configuration, the parallelarchitecture PATCNLSTM model achieves the best performance among nine models. Compared with the corresponding stackedarchitecture model, it reduces the mean absolute error （MAE） by 10.89%, the root mean square error （RMSE） by 5.96%, increases the coefficient of determination （R²） by 0.35%, and shortens the training time by 31.99%. Compared with single models, the PATCNLSTM model reduces MAE by 27.76% on average, RMSE by 35.17% on average, and increases R² by 1.20% on average. Furthermore, a comparative analysis of the prediction performance and efficiency of hybrid parallel and dualparallel architecture models is conducted using TPE Bayesian optimization. The results indicate that among the compared models developed in this study, TPEPATCNLSTM achieves the best prediction accuracy and training efficiency.

Key words

wind power / prediction models / deep learning / stacked deep learning model / parallel deep learning model / hyperparameter optimization

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Liu Shujun, Yu Xiaoqing, Du Xiaoze, Wu Jiangbo. RESEARCH ON ULTRA-SHORT-TERM WIND POWER FORECASTING MODELS BASED ON HYBRID DEEP LEARNING[J]. Acta Energiae Solaris Sinica. 2026, 47(7): 222-230 https://doi.org/10.19912/j.0254-0096.tynxb.2025-0449

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