BOOST-RPF: Boosted Sequential Trees for Radial Power Flow

TL;DR

BOOST-RPF introduces a path-based gradient-boosted decision tree approach for radial power flow analysis, achieving superior accuracy, robustness, and scalability compared to traditional neural network models, especially under topological changes.

Contribution

The paper presents a novel sequential path-based learning framework using gradient-boosted trees for power flow prediction, improving generalization and computational efficiency in distribution systems.

Findings

State-of-the-art accuracy on benchmark grids

Robust performance under topological shifts

Linear computational scaling with system size

Abstract

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.