By Topic

An artificial neural network hourly temperature forecaster with applications in load forecasting

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

4 Author(s)
A. Khotanzad ; Dept. of Electr. Eng., Southern Methodist Univ., Dallas, TX, USA ; M. H. Davis ; A. Abaye ; D. J. Maratukulam

Many power system short-term load forecasting techniques use forecast hourly temperatures in generating a load forecast. Some utility companies, however, do not have access to a weather service that provides these forecasts. To fill this need, a temperature forecaster, based on artificial neural networks, has been developed that predicts hourly temperatures up to seven days ahead. The prediction is based on forecast daily high and low temperatures and other information that would be readily available to any electric utility. The forecaster has been evaluated using data from eight utilities in the USA. The mean absolute error of one day ahead forecasts for these utilities is 1.48°F. The forecaster is implemented at several electric utilities and is being used in production environments

Published in:

IEEE Transactions on Power Systems  (Volume:11 ,  Issue: 2 )