摘要： 电力数据预测是电力系统态势感知与调度决策的基础。然而，现有电力预测方法在多时间尺度特征建模以及非结构化领域知识的有效融合等方面仍面临显著挑战，制约了模型在复杂电力系统场景中的预测精度与泛化能力。为此，本文提出一种融合大语言模型与知识图谱的电力数据智能预测方法LLM-KGAP（LLM enhanced Knowledge Graph Augmented Power prediction），构建数据–知识双驱动协同预测框架。首先，利用大语言模型从电力文档中自动抽取关键实体及因果关系，构建异构知识图谱；其次，设计一种基于语义置信度的知识映射机制，将图谱中的多路径语义关系转化为带权先验邻接矩阵，为预测模型提供知识引导的结构先验信息；最后，提出基于混合邻接矩阵的自适应时空信息提取网络（ASIEN-MAM），该网络采用渐进式分块策略实现多尺度时间窗口划分，并设计稀疏注意力xLSTM模块（SA-xLSTM），在时间维度上筛选关键时序片段并提取多尺度特征，同时融合先验知识与数据驱动的混合邻接矩阵，精确刻画电力系统中复杂的时空依赖关系。实验结果表明，所提方法在公开光伏数据集和区域负荷数据集上均显著优于对比方法，平均绝对误差降低11.9%–44.3%，平均绝对百分比误差降低7.0%–27.3%。

Abstract: Power data prediction is the foundation for situational awareness and dispatch decision-making in power systems. However, existing power prediction methods still face significant challenges in multi-scale temporal feature modeling and effective integration of unstructured domain knowledge, which limit the prediction accuracy and generalization capability of models in complex power system scenarios. To address these issues, this paper proposes an intelligent power data prediction method named LLM-KGAP (LLM enhanced Knowledge Graph Augmented Power prediction), which integrates large language models with knowledge graphs to construct a data-knowledge dual-driven collaborative prediction framework. First, a large language model is employed to automatically extract key entities and causal relationships from power-related documents to construct a heterogeneous knowledge graph. Second, a knowledge mapping mechanism based on semantic confidence is designed to transform multi-path semantic relationships in the knowledge graph into a weighted prior adjacency matrix, providing knowledge-guided structural prior information for the prediction model. Finally, an Adaptive Spatio-Temporal Information Extraction Network with Mixed Adjacency Matrix (ASIEN-MAM) is proposed. This network employs a progressive segmentation strategy to achieve multi-scale temporal window partitioning and designs a Sparse Attention-xLSTM (SA-xLSTM) module to filter key temporal segments and extract multi-scale features in the temporal dimension, while integrating prior knowledge with data-driven mixed adjacency matrices to accurately characterize complex spatio-temporal dependencies in power systems. Experimental results demonstrate that the proposed method significantly outperforms comparative methods on both public photovoltaic datasets and regional load datasets, reducing the mean absolute error by 11.9%–44.3% and the mean absolute percentage error by 7.0%–27.3%.