【摘要】 针对光伏输出功率波动显著且预测难度较大的问题，提出一种基于相似日聚类的WOA-VMD-Transformer的组合光伏功率预测模型。首先，利用K-means++算法进行相似日聚类；然后，采用鲸鱼优化算法（WOA）对变分模态分解（VMD）的参数进行寻优，将光伏功率序列分解为多个本征模态函数（IMF）；将IMF分量和气象因子加权合并成新的特征向量输入后续模型；并基于TCN-Transformer模型，分别预测不同天气类型下的IMF，叠加后得到预测值。最后，以澳大利亚中部爱丽丝泉沙漠太阳能研究中心的Hanwha Solar光伏场站一年的光伏发电和气象数据作为实例，对模型的有效性加以验证。消融实验和综合评估表明，所提模型在各类天气下均可取得较高的预测精度。更多还原

【Abstract】 To solve the problem that PV power fluctuates significantly and is difficult to predict, this paper proposes a combined PV power prediction model based on similar day clustering and an WOA-WMD-TCN-Transformer model. Firstly, K-means ++ is used to cluster similar days. Then WOA was used to optimize VMD parameters, and the PV power sequence was decomposed into multiple Intrinsic Mode functions(IMFs). The IMF components and meteorological factors were weighted and combined into a new feature vector and fed into the subsequent model. Based on TCN-Transformer, IMF under different weather conditions can be predicted separately and the predicted value can be obtained after superposition. Finally, the photovoltaic power generation and meteorological data of Hanwha Solar Photovoltaic Station, a desert solar Research Center in Alice Springs, Central Australia, were used as an example to verify the validity of the model. Ablation experiments and comprehensive evaluation show that the proposed model can achieve high prediction accuracy under various weather conditions.更多还原