由于可再生能源在能源行业中的地位日益突出，精准的风电功率预测变得越来越重要。基于多层感知器的深度学习预测方法存在过拟合风险，且难以捕捉长期依赖关系，传统的解决办法多为增加数据量、调整模型参数、使用更复杂的神经网络等，但效果有限。针对上述问题，研究了多层感知器在频域上的特征提取能力，提出了结合频域特征提取与自适应频率选择算法的策略，通过动态调整模型训练过程中各频率分量的权重来选择对预测贡献较大的特征。该策略有效减轻了过拟合现象，提升了模型对长期依赖关系的捕捉能力，并显著提高了预测性能与效率。最后进行两组实例验证，结果表明提出的方法比传统的深度学习预测模型拥有更高的预测精度。

Due to the growing importance of renewable energy in the energy industry, accurate wind power forecasting has become increasingly crucial. The deep learning forecasting method based on MLP (multi-layer perceptron) is prone to overfitting and struggles to capture long-term dependencies. Traditional solutions mainly involve increasing the dataset size, adjusting model parameters, or using more complex neural networks, but their effectiveness has been limited. To address these issues, the feature extraction capability of MLP in the frequency domain was studied, and a strategy combining frequency domain feature extraction with an adaptive frequency selection algorithm was proposed. This strategy dynamically adjusts the weights of the frequency components during model training to select the features that contribute most to the prediction. It effectively mitigates overfitting, enhances the model's ability to capture long-term dependencies, and significantly improves both the prediction accuracy and efficiency. Finally, two sets of case studies were conducted, and the results demonstrate that the proposed method outperforms traditional deep learning forecasting models in terms of prediction precision.