针对光伏发电预测中存在的数据噪声干扰、特征尺度差异及多尺度气象模式建模不足等问题，提出一种动态数据预处理与门控密集多尺度卷积神经网络 (gated dense multiscale convolutional neural network，GDMSCNN) 的预测方法。首先，建立基于动态滑动窗口 Z 分数的异常检测机制，结合协方差加权多变量插值处理缺失值；其次，采用自适应分段归一化算法消除特征量纲差异，并构造云量修正因子与大气衰减因子来增强物理特征表达；最后，设计 GDMS-CNN 网络，通过深度可分离卷积模块优化特征提取效率，构建密集连接扩张卷积块来捕获多尺度时空关联特征，嵌入非对称门控通道注意力机制以动态校准特征权重。实验结果表明：所提方法在均方根误差 (root mean square error，RMSE) 上较最优基准模型遗传算法-变模态分解-回声状态网络 (genetic algorithm - variational mode decomposition - echo state network，GA-VMD-ESN) 降低 16.4%，较传统随机森林法降低 43.4%。该方法为光伏出力预测提供了新的解决方案，有效提升了电网调度的可靠性。

To  address  the  issues  of  data  noise  interference,  feature  scale  discrepancy,  and  insufficient  multiscale meteorological  pattern  modeling  in  photovoltaic  power  forecasting,  a  prediction  method  based  on  dynamic  data preprocessing  and  gated  dense  multiscale  convolutional  neural  network  (GDMS-CNN)  is  proposed.  Firstly,  an  anomaly detection  mechanism  based  on  dynamic  sliding-window  Z-score  is  established,  and  missing  values  are  processed  via covariance-weighted  multivariate  interpolation.  Secondly,  an  adaptive  piecewise  normalization  algorithm  is  adopted  to eliminate  feature  dimensional  differences,  and  cloud-cover  correction  factor  and  atmospheric  attenuation  factor  are constructed to enhance physical feature representation. Finally, a GDMS-CNN is designed, wherein the feature extraction efficiency  is  optimized  by  depthwise  separable  convolution  modules,  densely  connected  dilated  convolution  blocks  are constructed to capture multiscale spatiotemporal correlation features, and an asymmetric gated channel attention mechanism is embedded to dynamically recalibrate feature weights. Experimental results demonstrate that the proposed method reduces the root mean square error (RMSE) by 16.4% compared with the optimal baseline model genetic algorithm-variational mode decomposition-echo  state  network  (GA-VMD-ESN),  and  by  43.4%  compared  with  the  traditional  random  forest.  The proposed method provides a novel solution for photovoltaic output forecasting and effectively enhances the reliability of power grid dispatching.