AI RESEARCH
Learning Power Flow with Confidence: A Probabilistic Guarantee Framework for Voltage Risk
arXiv CS.LG
•
ArXi:2308.07867v4 Announce Type: replace-cross The absence of formal performance guarantees in machine learning (ML) has limited its adoption for safety-critical power system applications, where confidence and interpretability are as vital as accuracy. In this work, we present a probabilistic guarantee for power flow learning and voltage risk estimation, derived through the framework of Gaussian Process (GP) regression.