准确预测风电功率对于提高电力系统的效率和安全性具有重要意义，而海上风能具有的间歇性和随机性的特点致使海上风电功率难以准确预测。因此，提出了一种名为Boruta-SSA-CKCformer的新模型，这是Crossformer模型架构的一种增强版本，特别针对海上风电功率预测进行了优化。该模型将Boruta特征筛选算法整合进模型前端以优化输入特征的选择，从而提高预测精度。模型进一步将卷积神经网络（convolutional neural network, CNN）集成到Crossformer模型的维度分段嵌入层（dimension-segment-wise embedding, DSW）中构建了卷积维度分段嵌入层（CNN-dimension-segment-wise embedding, CDSW），有效地捕捉了多元时间序列数据中的局部特征和跨维度依赖关系。此外，该模型在两阶段注意力层（two-stage attention, TSA）中引入了柯尔莫哥洛夫-阿诺德网络（Kolmogorov-Arnold networks, KAN）连接，构建了基于柯尔莫哥洛夫-阿诺德网络的两阶段注意力层（KAN-two-stage attention, KTSA），以此来弥补Crossformer模型处理非高斯噪声方面的不足。最后，使用麻雀搜索算法（sparrow search algorithm, SSA）对模型参数进行优化以进一步提高模型预测精度。通过对福建海上风电场数据的实证研究，Boruta-SSA-CKCformer模型相较于其他模型，在海上风电功率预测方面展现出了更佳的性能。

Accurate prediction of wind power is significant for enhancing the efficiency and safety of power systems. Offshore wind energy is intermittency and randomness, making offshore wind power prediction challenging. A new model named Boruta-SSA-CKCformer, an enhanced version of the Crossformer model architecture, was proposed for optimizing offshore wind power prediction. The Boruta feature selection algorithm was integrated into the front end of the model to optimize input feature selection, thus improving prediction accuracy. The convolutional neural network was incorporated into the dimension-segment-wise embedding layer of the Crossformer model, forming the convolutional dimension-segment-wise embedding layer(CDSW). This effectively captured local features and cross-dimensional dependencies in multivariate time series data. Kolmogorov-arnold networks(KAN) connections were added to the two-stage attention layer, establishing the KAN-based two-stage attention layer (KTSA), which addressed deficiencies in non-Gaussian noise processing by the Crossformer model. Model parameters were optimized using the sparrow search algorithm(SSA) to further improve prediction accuracy. Empirical studies on data from offshore wind farms in Fujian were carried out. The Boruta-SSA-CKCformer model shows superior performance in offshore wind power prediction compared to other models.