By Topic

Identification of stochastic electric load models from physical data

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
$33 $13
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)
F. Galiana ; University of Michigan, Ann Arbor, MI, USA ; E. Handschin ; A. Fiechter

The three step identification process of model development, parameter estimation, and performance analysis is illustrated through the identification of models for the prediction of electric power demand. Each step is carefully supported by numerical results based on physical data. Three types of progressively more complex but more accurate load models are identified which describe 1) time periodicity, 2) time periodicity plus load autocorrelation, and 3) time periodicity plus load autocorrelation plus dynamic temperature effects. Accurate predictions up to one week are demonstrated. General guidelines are extrapolated from this identification example when possible.

Published in:

IEEE Transactions on Automatic Control  (Volume:19 ,  Issue: 6 )