Storm Power Outage Prediction and Verification using NWP Models and Remote Sensing Data

Abstract

In this manuscript, we discuss the importance of using accurate weather forecasts based on remote sensing observations for improving the accuracy of electric distribution grid power outage prediction. Weather variable accuracy is critical to limit the significant power outage modeling errors arising from the strong non-linearities in the relationships between hydrometeorological variables and power outages. We consolidate recent findings related to the most important variables for outage prediction, and to the propagation of their uncertainties in the University of Connecticut Outage Prediction Model (UConn-OPM). UConn-OPM is an operational machine learning-based framework predicting power outage in electric distribution networks. Combining remote sensing datasets with weather analysis used for OPM training, and post-storm verification, would directly contribute to improving the OPM prediction accuracy.