Cart (Loading....) | Create Account
Close category search window
 

A statistical model for risk management of electric outage forecasts

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $31
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)

Risk management of power outages caused by severe weather events, such as hurricanes, tornadoes, and thunderstorms, plays an important role in electric utility distribution operations. Damage prediction based on weather forecasts on an appropriate spatial scale can improve the efficiency of risk management by reducing the economic and societal costs associated with restoration efforts. We have developed a method of predicting the number of outages in a fashion that is suitable for use by electric utilities by using a Poisson regression model for spatial data in a Bayesian hierarchical framework. Particular attention is given to building models that incorporate uncertainty in the outage data from the perspective of multiple spatial resolutions and spatial correlation in the outage data. The outage-prediction model was developed using historical outage data from an electric utility company in the northeastern part of the United States. The model is being used by that company in the operations of its overhead electrical distribution system and emergency management operations. We discuss results to date and how the model is being applied. In addition to the damage forecasts, we have developed tools for risk visualization by displaying the uncertainty of the damage forecasts on geographic maps.

Note: The Institute of Electrical and Electronics Engineers, Incorporated is distributing this Article with permission of the International Business Machines Corporation (IBM) who is the exclusive owner. The recipient of this Article may not assign, sublicense, lease, rent or otherwise transfer, reproduce, prepare derivative works, publicly display or perform, or distribute the Article.  

Published in:

IBM Journal of Research and Development  (Volume:54 ,  Issue: 3 )

Date of Publication:

May-june 2010

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.