Two-Stage Short-Term Wind Power Prediction based on Improved CNN-BiLSTM-Attention

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. 为了提升短期风电功率预测的准确性，本文研究了一种基于逐次变分模态分解（SVMD）方法、贝叶斯优化（BO）算法和CNN-BiLSTM-Attention模型结合的两阶段短期风电功率预测方法。该方法具体原理为：在第一阶段，利用SVMD方法对经过数据预处理后的风电功率数据进行分解，将得到的模态分量作为CNN-BiLSTM-Attention短期功率预测模型的输入，然后引入BO算法对预测模型的学习率、隐含层节点和正则化参数进行调优，利用训练好的模型进行预测，得到初始风电功率预测值；在第二阶段，采用预测模型对误差序列进行误差补偿预测，得到初始误差功率预测数据，将初始预测功率和误差预测功率求和得到最终的风电功率预测结果。结果表明：相比于CNN-BiLSTM-Attention模型，改进CNN-BiLSTM-Attention模型两阶段预测模型的MAE值、MAPE值和RMSE值分别下降了85.7%、75.2%和77.3%，表明了本文研究的改进CNN-BiLSTM-Attention模型的两阶段短期风电功率预测方法的有效性。