[2603.21977] BOOST-RPF: Boosted Sequential Trees for Radial Power Flow

Computer Science > Machine Learning arXiv:2603.21977 (cs) [Submitted on 23 Mar 2026] Title:BOOST-RPF: Boosted Sequential Trees for Radial Power Flow Authors:Ehimare Okoyomon, Christoph Goebel View a PDF of the paper titled BOOST-RPF: Boosted Sequential Trees for Radial Power Flow, by Ehimare Okoyomon and 1 other authors View PDF HTML (experimental) 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 perfo...