Accurate photovoltaic (PV) power forecasting is critical for integrating renewable energy sources into the grid, optimizing real-time energy management, and ensuring energy reliability amidst increasing demand. However, existing models often struggle with effectively capturing the complex relationships between target variables and covariates, as well as the interactions between temporal dynamics and multivariate data, leading to suboptimal forecasting accuracy. To address these challenges, we propose a novel model architecture that leverages the iTransformer for feature extraction from target variables and employs long short-term memory (LSTM) to extract features from covariates. A cross-attention mechanism is integrated to fuse the outputs of both models, followed by a Kolmogorov-Arnold network (KAN) mapping for enhanced representation. The effectiveness of the proposed model is validated using publicly available datasets from Australia, with experiments conducted across four seasons. Results demonstrate that the proposed model effectively capture seasonal variations in PV power generation and improve forecasting accuracy.

翻译：准确的光伏功率预测对于将可再生能源接入电网、优化实时能源管理以及在日益增长的能源需求中确保供电可靠性至关重要。然而，现有模型往往难以有效捕捉目标变量与协变量之间的复杂关系，以及时序动态与多元数据之间的交互作用，导致预测精度欠佳。为应对这些挑战，本文提出一种新颖的模型架构：利用iTransformer从目标变量中提取特征，并采用长短期记忆网络从协变量中提取特征。通过集成交叉注意力机制融合两个模型的输出，再经Kolmogorov-Arnold网络映射以增强特征表示。使用澳大利亚的公开数据集对所提模型的有效性进行了验证，并开展了跨四季的实验。结果表明，所提模型能有效捕捉光伏发电的季节性变化，并提升了预测精度。