Quantifying Uncertainty in Machine Learning-Based Power Outage Prediction Model Training: A Tool for Sustainable Storm Restoration

Quantifying Uncertainty in Machine Learning-Based Power Outage Prediction Model Training: A Tool for Sustainable Storm Restoration

A growing number of electricity utilities use machine learning-based outage prediction models (OPMs) to predict the impact of storms on their networks for sustainable management. The accuracy of OPM predictions is sensitive to sample size and event severity representativeness in the training dataset...

Full description

Bibliographic Details
Main Authors: Feifei Yang, David W. Wanik, Diego Cerrai, Md Abul Ehsan Bhuiyan, Emmanouil N. Anagnostou
Format: Article
Language:English
Published: MDPI AG 2020-02-01
Series:Sustainability
Subjects:
cross entropy
event severity representativeness
machine learning
outage prediction model
sample size
severe weather
uncertainty
Online Access:https://www.mdpi.com/2071-1050/12/4/1525

Similar Items

Similar Items