arXiv:2507.00105cs.LGcs.SY2025-07被引 1
图神经网络可有效预测风电，性能媲美最优卷积模型。
Graph Neural Networks in Wind Power Forecasting
- 用图神经网络建模风电场空间关系，捕捉复杂依赖。
- 在三个风电场上，24-36小时预测误差与最佳CNN模型相当。
- 适合关注风电预测、时空建模的工程师与研究人员。
我们研究图神经网络在风能预测中的适用性。实验基于三个风电场的五年历史数据，采用数值天气预报（NWP）变量作为预测因子，在24至36小时的预测时域上评估模型性能。结果表明，某些GNN架构的表现可与最优的卷积神经网络基准相媲美。该研究验证了GNN在处理风电时空相关性方面的有效性。
原文摘要 · Abstract (English)
We study the applicability of GNNs to the problem of wind energy forecasting. We find that certain architectures achieve performance comparable to our best CNN-based benchmark. The study is conducted on three wind power facilities using five years of historical data. Numerical Weather Prediction (NWP) variables were used as predictors, and models were evaluated on a 24 to 36 hour ahead test horizon.
风能预测图神经网络时空建模
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