融合电网物理特性与时间序列，提升电力负荷预测精度
Enhanced Load Forecasting with GAT-LSTM: Leveraging Grid and Temporal Features
- 用图注意力网络捕捉电网空间关系，融入线路容量等实际参数
- 在巴西电网数据上，误差比现有方法降低20%以上
- 适合电网调度、能源规划等需要高精度预测的场景
精准的电力负荷预测对电网高效运行与规划至关重要，尤其在可再生能源带来更大波动性的背景下。本文提出GAT-LSTM模型，结合图注意力网络（GAT）与长短期记忆网络（LSTM）。该模型创新性地将线路容量、效率等边属性引入注意力机制，使空间关系学习基于电网实际物理与运行约束。通过早期融合空间图嵌入与时间序列特征，模型有效捕捉空间依赖与时间模式间的复杂交互，实现对电网动态的更真实建模。在巴西电力系统数据集上的实验表明，该模型显著优于现有先进方法，实现MAE降低21.8%、RMSE降低15.9%、MAPE降低20.2%。结果证明了GAT-LSTM的鲁棒性与适应性，是电网管理与能源规划的重要工具。
原文摘要 · Abstract (English)
Accurate power load forecasting is essential for the efficient operation and planning of electrical grids, particularly given the increased variability and complexity introduced by renewable energy sources. This paper introduces GAT-LSTM, a hybrid model that combines Graph Attention Networks (GAT) and Long Short-Term Memory (LSTM) networks. A key innovation of the model is the incorporation of edge attributes, such as line capacities and efficiencies, into the attention mechanism, enabling it to dynamically capture spatial relationships grounded in grid-specific physical and operational constraints. Additionally, by employing an early fusion of spatial graph embeddings and temporal sequence features, the model effectively learns and predicts complex interactions between spatial dependencies and temporal patterns, providing a realistic representation of the dynamics of power grids. Experimental evaluations on the Brazilian Electricity System dataset demonstrate that the GAT-LSTM model significantly outperforms state-of-the-art models, achieving reductions of 21. 8% in MAE, 15. 9% in RMSE and 20. 2% in MAPE. These results underscore the robustness and adaptability of the GAT-LSTM model, establishing it as a powerful tool for applications in grid management and energy planning.
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