准确的光伏功率预测结果对于电网的调度优化具有重要意义，针对现有预测方法仅侧重于时域或频域特征，导致未能充分提取光伏数据中时频域信息。提出一种融合时频特征的光伏功率预测模型时域与频域注意力特征融合（time-domain and frequency-domain attentional feature fusion, TDFDAFF），旨在更全面地捕捉光伏序列数据的复杂特性。首先，对原始的光伏序列数据进行特征相关性分析，选取与光伏发电功率强相关的气象因素作为输入特征，并将筛选后的特征数据采用滑动窗口技术进行分割，构建输入特征矩阵。随后分别输入到时序特征提取模块、频率特征提取模块来提取光伏序列数据中的时频特征。最后，采用注意力特征融合模块对两部分提取到的时频特征进行融合，为每个特征动态的赋予不同的权重，从而强调对重要特征的关注，将融合后的特征通过全连接层进行映射得到最终的预测输出。通过在两个地区的光伏数据集上进行验证，TDFDAFF模型相较于其他预测模型有着更好的预测精度。

Accurate photovoltaic power prediction results are of great significance for the dispatching optimization of the power grid. Therefore, the existing prediction methods only focus on the time domain or frequency domain characteristics, resulting in the failure to fully extract the time and frequency domain information in the photovoltaic data. A photovoltaic power prediction model TDFDAFF integrated with time-frequency features was proposed, aiming to capture the complex characteristics of photovoltaic sequence data more comprehensively. First, the feature correlation analysis was conducted on the original photovoltaic sequence data was conducted, and the meteorological factors strongly related to the photovoltaic power were selected as the input features, and the filtered feature data were divided by sliding window technology to build the input feature matrix. Subsequently, it was input to the timing feature extraction module and the frequency feature extraction module respectively to extract the time-frequency features in the photovoltaic sequence data. Finally, the attention feature fusion module was used to fuse the time-frequency features extracted from the two parts, and assign different weights to the dynamics of each feature, so as to emphasize the attention of important features, and map the fused features through the full connection layer to obtain the final prediction output. Through validation on photovoltaic datasets in two regions, the TDFDAFF model has better prediction accuracy compared with other prediction models.