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Evaluating Machine Learning Models for the Fast Identification of Contingency Cases

Evaluating Machine Learning Models for the Fast Identification of Contingency Cases

Florian Schaefer
J. Menke
M. Braun
ArXiv (abs)PDFHTML

Papers citing "Evaluating Machine Learning Models for the Fast Identification of Contingency Cases"

4 / 4 papers shown
Title
Fast Power system security analysis with Guided Dropout
Fast Power system security analysis with Guided Dropout
Benjamin Donnot
Isabelle M Guyon
Marc Schoenauer
Antoine Marot
P. Panciatici
58
29
0
30 Jan 2018
Supervised Learning for Optimal Power Flow as a Real-Time Proxy
Supervised Learning for Optimal Power Flow as a Real-Time Proxy
Raphaël Canyasse
Gal Dalal
Shie Mannor
40
39
0
20 Dec 2016
XGBoost: A Scalable Tree Boosting System
XGBoost: A Scalable Tree Boosting System
Tianqi Chen
Carlos Guestrin
809
39,062
0
09 Mar 2016
SMOTE: Synthetic Minority Over-sampling Technique
SMOTE: Synthetic Minority Over-sampling Technique
Nitesh Chawla
Kevin W. Bowyer
Lawrence Hall
W. Kegelmeyer
AI4TS
386
25,686
0
09 Jun 2011