Two-Stage Short-Term Wind Power Prediction based on Improved CNN-BiLSTM-Attention
1. Guodian and Wind Power Development Co., Ltd, Shenyang 110000, China
2. School of Mechanical and Electrical Engineering, Harbin Institute of Technology, Harbin 150001, China
| Abstract: | To enhance the accuracy of short-term wind power prediction, this paper proposes a novel two-stage forecasting framework that integrates Sequential Variational Mode Decomposition (SVMD), Bayesian Optimization (BO), and a CNN-BiLSTM-Attention model. In the first stage, the preprocessed wind power historical data is decomposed into several modal components via SVMD. These components serve as inputs to the CNN-BiLSTM-Attention model, whose hyperparameters—including the learning rate, number of hidden units, and regularization coefficient—are automatically tuned using the BO algorithm. The output of this stage is the initial power prediction. In the second stage, the prediction error sequence from the first stage is analyzed and similarly processed (decomposed and modeled) to generate an error compensation term. The final prediction is obtained by summing the initial power prediction and the predicted error compensation. Results show that compared with the CNN-BiLSTM-Attention model, the MAE, MAPE and RMSE values of the improved CNN-BiLSTM-Attention two-stage prediction model decreased by 85.7%, 75.2% and 77.3%, respectively, demonstrating the effectiveness of the two-stage short-term wind power prediction method of the improved CNN-BiLSTM-Attention model studied in this paper. |
| Keywords: | Short-term Wind Power Forecasting; Successive Variational Mode Decomposition; Bayesian Optimization Algorithm; Error Compensation; forecastIng Model |
| DOI: | 10.57237/j.jsts.2025.02.002 |
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Received: 18 October 2025
Accepted: 8 December 2025
Published Online: 24 December 2025
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