Interpretable Machine Learning for Power Systems: Establishing Confidence in SHapley Additive exPlanations

no code implementations • 13 Sep 2022 • Robert I. Hamilton, Jochen Stiasny, Tabia Ahmad, Samuel Chevalier, Rahul Nellikkath, Ilgiz Murzakhanov, Spyros Chatzivasileiadis, Panagiotis N. Papadopoulos

To do so, we demonstrate that the Power Transfer Distribution Factors (PTDF) -- a physics-based linear sensitivity index -- can be derived from the SHAP values.

Interpretable Machine Learning