This paper reconsiders end-to-end learning approaches to the Optimal Power Flow (OPF). Existing methods, which learn the input/output mapping of the OPF, suffer from scalability issues due to the high dimensionality of the output space. This paper first shows that the space of optimal solutions can be significantly compressed using principal component analysis (PCA). It then proposes Compact Learning, a new method that learns in a subspace of the principal components before translating the vectors into the original output space. This compression reduces the number of trainable parameters substantially, improving scalability and effectiveness. Compact Learning is evaluated on a variety of test cases from the PGLib with up to 30,000 buses. The paper also shows that the output of Compact Learning can be used to warm-start an exact AC solver to restore feasibility, while bringing significant speed-ups.

翻译：本文重新审视了针对最优潮流（OPF）的端到端学习方法。现有方法通过学习OPF的输入/输出映射，因输出空间的高维性而面临可扩展性问题。本文首先证明，利用主成分分析（PCA）可显著压缩最优解空间。随后提出了一种新方法——紧致学习（Compact Learning），该方法在主成分子空间中进行学习，再将向量转换回原始输出空间。这种压缩极大地减少了可训练参数的数量，从而提升了可扩展性和有效性。紧致学习在来自PGLib的多种测试案例上进行了评估，其中包含高达30000个节点的系统。本文还表明，紧致学习的输出可用于热启动精确交流求解器以恢复可行性，同时显著提升求解速度。