By Topic

Forecasting several-hours-ahead electricity demand using neural network

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

4 Author(s)
Mandal, P. ; Dept. of Electr. & Electron. Eng., Ryukyus Univ., Okinawa, Japan ; Senjyu, T. ; Uezato, K. ; Funabashi, T.

This paper presents a practical method for short-term load forecasting considering the temperature as climate factor. The method is based on artificial neural network (ANN) combined similar days approach, which achieved a good performance in the very special region. Performance of the proposed methodology is verified with simulations of actual data pertaining to Okinawa Electric Power Co. in Japan. Forecasted load is obtained from ANN, which is the corrected output of similar days data. Load curve is forecasted by using information of the days being similar to weather condition of the forecast day. An Euclidean norm with weighted factors is used to evaluate the similarity between a forecast day and searched previous days. Special attention was paid to model accurately in different seasons, i.e., summer, winter, spring, and autumn. Moreover, the forecaster is robust, easy to use, and produces accurate results in the case of rapid weather changes.

Published in:

Electric Utility Deregulation, Restructuring and Power Technologies, 2004. (DRPT 2004). Proceedings of the 2004 IEEE International Conference on  (Volume:2 )

Date of Conference:

5-8 April 2004