arXiv Machine Learning July 21, 2026 By Daniel Flores, Yuanrui Sang, Michael P. McGarry

Increasing Line Outage Localization Performance with Ensemble Classifiers

Read the original on arXiv Machine Learning →

arXiv:2607. 17008v1 Announce Type: cross Abstract: In many cases, the outage of one transmission line in a system can be localized by monitoring the power flow of another line, and machine learning methods can be used to distinguish the cases under uncertainty.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

agents

arXiv AI

Jun 19

Controlled Comparison of Machine Learning Models for Fault Classification and Localization in Power System Protection

arXiv:2510. 00831v2 Announce Type: replace Abstract: The increasing complexity of modern power systems, driven by the integration of inverter-based and distributed energy resources, challenges the reliability of conventional protection schemes and motivates the use of machine learning for protection tasks.

By Julian Oelhaf, Georg Kordowich, Changhun Kim, Paula Andrea P\'erez-Toro, Christian Bergler, Andreas Maier, Johann J\"ager, Siming Bayer

arXiv Machine Learning

Aug 12

A Systematic Sample Size Analysis of ML-Based Path Loss Prediction for LPWAN

arXiv:2608. 11083v1 Announce Type: cross Abstract: Low Power Wide Area Networks like LoRa are increasingly deployed for smart city applications, requiring accurate path loss prediction for effective network planning.

By Robert Bitterling, Christian Nettersheim, J\"orn Hees, Michael Rademacher

fine-tuning

arXiv Machine Learning

Aug 3

POSSE-kNN: Pathwise Out-of-Bag Selected Subspace Ensembles for Binary Classification

arXiv:2211. 11278v3 Announce Type: replace-cross Abstract: Nearest neighbour classification is attractive for tabular data, but its performance can deteriorate when a fixed query centred neighbourhood does not follow the local class geometry.

By Zardad Khan, Amjad Ali, Najd Adeed, Saeed Aldahmani

benchmarks

arXiv AI

1d ago

Hoeffding adaptive splitting trees for data stream classification with concept drift and ensemble learning

arXiv:2608. 16659v1 Announce Type: cross Abstract: Ensembles of decision trees are well-established methods for data stream classification.

By Daniel Nowak Assis, Jean Paul Barddal, Fabr\'icio Enembreck

benchmarks

arXiv Machine Learning

Aug 11

Full-Feature versus Limited-Input Machine Learning for Residential Energy Estimation: A Comparative Analysis of RECS and ResStock Under Realistic Input Constraints

arXiv:2608. 09255v1 Announce Type: new Abstract: Residential energy estimates are often needed before detailed envelope characteristics, equipment efficiencies, infiltration, sensor, or billing data are available.

By Aditya Ramnarayan, Fatih Evren, Patti Gunderson, Samuel Rosenberg

benchmarks

arXiv Machine Learning

5d ago

Post-Hoc Uncertainty-Aware Explanations for Deployed Power Quality Disturbance Classifiers via Laplace Approximation

arXiv:2604. 13658v2 Announce Type: replace Abstract: Deep learning classifiers achieve high accuracy in power quality disturbance (PQD) recognition, but existing explanation methods return a single deterministic attribution map and provide no measure of its reliability.

By Yinsong Chen, Samson S. Yu, Kashem M. Muttaqi

benchmarks

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Controlled Comparison of Machine Learning Models for Fault Classification and Localization in Power System Protection

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