融合风电动态特征与通道注意力的超短期风速混合预测

Ultra-Short-Term Wind Speed Hybrid Prediction with Wind Power Dynamic Features and Channel Attention

准确的风速预测对于保障电网的稳定性和提升运行效率至关重要。为了提高预测的准确性,提出一种融合风电动态特征与通道注意力的超短期风速预测混合模型。首先,考虑气象因素对风速变化的影响,融合气象数据的静态和动态特征构建特征矩阵,深入挖掘影响风速的关键潜在因素。然后,采用时变滤波经验模态分解对原始风速进行初步分解,随后应用变分模态分解对高频分量进一步分解,以降低数据的不稳定性并增强模型的可预测性。其次,为每个子序列分别构建双向长短期记忆网络预测模型,并引入高效通道注意力机制,以自适应地为多通道特征信息分配权重,使模型能够集中于关键特征信息,从而提高模型的预测精度。最后,通过综合各子模型输出得到最终的风速预测值。实例分析表明,所提模型具有较好的预测精度和鲁棒性。

Accurate wind speed prediction is crucial for ensuring the stability of the power grid and enhancing operational efficiency.To improve the accuracy of predictions,a hybrid model for ultra-short-term wind speed prediction is proposed which integrates wind power dynamic features and channel attention.Firstly,considering the impact of meteorological factors on wind speed variations,a feature matrix is constructed by integrating static and dynamic features of meteorological data,thereby exploring the key underlying factors influencing wind speed.Then,the time-varying filtering empirical modal decomposition is employed to preliminarily decompose the original wind speed,followed by variational mode decomposition to further decompose high-frequency components to reduce the instability of data and enhance the predictability of the model.Subsequently,bidirectional long and short-term memory network prediction models are constructed for each subsequence separately,and an efficient channel attention mechanism is incorporated to assign weights to the multi-channel feature information adaptively,so that the model can focus on the key feature information and thereby improves the prediction accuracy.Finally,the final wind speed prediction values is obtained by integrating the output of all sub-models.Case studies demonstrate that the proposed model achieves superior prediction accuracy and robustness.