融合时序与变量交互，提升光伏功率预测精度
Enhanced Photovoltaic Power Forecasting: An iTransformer and LSTM-Based Model Integrating Temporal and Covariate Interactions
- 用iTransformer和LSTM分别提取目标与协变量特征
- 跨注意力融合+KAN映射，显著降低预测误差
- 适合能源管理、电网调度等实际场景应用
准确的光伏（PV）功率预测对可再生能源并网、实时能源管理及保障能源可靠性至关重要。然而，现有模型难以有效捕捉目标变量与协变量之间的复杂关系，以及时间动态与多变量数据间的交互，导致预测精度不足。为此，本文提出一种新模型架构：利用iTransformer从目标变量中提取特征，采用长短期记忆网络（LSTM）从协变量中提取特征，并通过交叉注意力机制融合两者输出，最后使用柯尔莫哥洛夫-阿诺德网络（KAN）进行增强表征。在澳大利亚公开数据集上，基于四个季节的实验验证了该模型能有效捕捉光伏发电的季节性变化，显著提升预测精度。
原文摘要 · Abstract (English)
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.
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