3 citations · 4 across the 2 of their papers we have counts for
2 papers
eess.SY2022★ 3 cited
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…
eess.SY2021★ 1 cited
Physics-Informed Neural Networks for Minimising Worst-Case Violations in DC Optimal Power Flow
Rahul Nellikkath, Spyros Chatzivasileiadis
Physics-informed neural networks exploit the existing models of the underlying physical systems to generate higher accuracy results with fewer data. Such approaches can help drasti…