164 citations · 213 across the 13 of their papers we have counts for
3 papers · 1 filter
Minimizing Worst-Case Violations of Neural Networks
Rahul Nellikkath, Spyros Chatzivasileiadis
Machine learning (ML) algorithms are remarkably good at approximating complex non-linear relationships. Most ML training processes, however, are designed to deliver ML tools with g…
Interpretable Machine Learning for Power Systems: Establishing Confidence in SHapley Additive exPlanations
Robert I. Hamilton, Jochen Stiasny, Tabia Ahmad +5
Interpretable Machine Learning (IML) is expected to remove significant barriers for the application of Machine Learning (ML) algorithms in power systems. This letter first seeks to…
Closing the Loop: A Framework for Trustworthy Machine Learning in Power Systems
Jochen Stiasny, Samuel Chevalier, Rahul Nellikkath +2
Deep decarbonization of the energy sector will require massive penetration of stochastic renewable energy resources and an enormous amount of grid asset coordination; this represen…