We are currently experiencing intermittent issues impacting performance. We apologize for the inconvenience.
By Topic

Load forecasting via suboptimal seasonal autoregressive models and iteratively reweighted least squares estimation

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 $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

2 Author(s)
Mbamalu, G.A.N. ; Tech. Univ. of Nova Scotia, Halifax, NS, Canada ; El-Hawary, M.E.

Suboptimal least squares or iteratively reweighted least-squares (IRWLS) procedures for estimating the parameters of a seasonal multiplicative autoregressive (AR) model encountered during power system load forecasting are proposed. The method involves using an interactive computer environment to estimate the parameters of a seasonal multiplicative AR process. The method comprises five major computational steps. The first determines the order of the seasonal multiplicative AR process, and the second uses the least squares or the IRWLS to estimate the optimal nonseasonal AR model parameters. In the third step one obtains the intermediate series by back forecast, which is followed by using the least squares or the IRWLS to estimate the optimal seasonal AR parameters. The final step uses the estimated parameters to forecast future load. The method is applied to predict the Nova Scotia Power Corporation's 168 lead time hourly load. The results obtained are documented and compared with results based on the Box and Jenkins method

Published in:

Power Systems, IEEE Transactions on  (Volume:8 ,  Issue: 1 )