针对短期负荷预测中指数平滑法的抗噪声性能差，和电力负荷数据的多重季节性等问题，提出了一种结合自适应滤波器和指数平滑法，并加入带注意力机制的膨胀循环网络的混合预测模型。首先，利用自适应频谱模块从频域的角度上去除噪声；其次，通过指数平滑法对负荷数据进行分解，去季节化，归一化和构建输入向量；最后，将膨胀循环神经网络与自注意力机制相结合，根据当前输入向量的成分动态地分配权重和捕捉负荷数据中的长短期依赖关系，并且还会通过学习指数平滑的参数，来提高预测的灵活性和准确性。实验结果表明，该模型有更好的抗噪声性能和更好的预测精度。

Aiming at the poor noise resistance of the exponential smoothing method in short-term load forecasting and the multiple seasonality of power load data, a hybrid forecasting model combining adaptive filters, exponential smoothing, and dilated recurrent neural networks with attention mechanisms was proposed. First, the adaptive spectral module was employed to remove noise from the frequency domain perspective. Subsequently, exponential smoothing was utilized to decompose the load data, remove seasonality, normalize the data, and construct the input vector. Finally, the dilated recurrent neural network combined with the self-attention mechanism dynamically allocates weights based on the components of the current input vector and captured the long-and short-term dependencies in the load data. Additionally, the model learned the parameters of exponential smoothing to enhance the flexibility and accuracy of the forecast. Experimental results demonstrate that the proposed model exhibits superior noise resistance and prediction accuracy.