Accurate power flow analysis is critical for modern distribution systems, yet classical solvers face scalability issues, and current machine learning models often struggle with generalization. We introduce BOOST-RPF, a novel method that reformulates voltage prediction from a global graph regression task into a sequential path-based learning problem. By decomposing radial networks into root-to-leaf paths, we leverage gradient-boosted decision trees (XGBoost) to model local voltage-drop regularities. We evaluate three architectural variants: Absolute Voltage, Parent Residual, and Physics-Informed Residual. This approach aligns the model architecture with the recursive physics of power flow, ensuring size-agnostic application and superior out-of-distribution robustness. Benchmarked against the Kerber Dorfnetz grid and the ENGAGE suite, BOOST-RPF achieves state-of-the-art results with its Parent Residual variant which consistently outperforms both analytical and neural baselines in standard accuracy and generalization tasks. While global Multi-Layer Perceptrons (MLPs) and Graph Neural Networks (GNNs) often suffer from performance degradation under topological shifts, BOOST-RPF maintains high precision across unseen feeders. Furthermore, the framework displays linear $O(N)$ computational scaling and significantly increased sample efficiency through per-edge supervision, offering a scalable and generalizable alternative for real-time distribution system operator (DSO) applications.

翻译：[translated abstract in Chinese] 准确的潮流分析对于现代配电系统至关重要，然而传统求解器面临可扩展性问题，当前机器学习模型在泛化能力上常显不足。本文提出BOOST-RPF，一种将电压预测从全局图回归任务重构为序贯路径学习问题的新方法。通过将辐射状网络分解为从根节点到叶节点的路径，我们利用梯度提升决策树（XGBoost）对局部电压降规律进行建模。我们评估了三种架构变体：绝对电压法、父节点残差法和物理信息残差法。该方法使模型架构与潮流的递归物理特性保持一致，确保规模无关的适用性和优越的分布外鲁棒性。在Kerber Dorfnetz电网与ENGAGE测试套件上的基准测试表明，BOOST-RPF的父节点残差变体取得了最优结果，在标准准确性和泛化任务中始终优于解析解和神经网络基线方法。当全局多层感知器（MLP）与图神经网络（GNN）常因拓扑变化出现性能退化时，BOOST-RPF在未见馈线场景下仍保持高精度。此外，本框架通过逐边监督实现线性$O(N)$计算复杂度，并显著提升样本效率，为实时配电系统运营商（DSO）应用提供了兼具可扩展性与泛化能力的替代方案。