By Topic

Design of artificial neural networks for short-term load forecasting. I. Self-organising feature maps for day type identification

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 $33
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)
Yuan-Yih Hsu ; Dept. of Electr. Eng., Nat. Taiwan Univ., Taipei, Taiwan ; Chien-Chuen Yang

A new approach using artificial neural networks (ANNs) is proposed for short-term load forecasting. To forecast the hourly loads of a day, the hourly load pattern and the peak and valley loads of the day must be determined. In part I, a neural network based on self-organising feature maps to identify those days with similar hourly load patterns is developed. These days with similar load patterns are said to be of the same day type. The load pattern of the day under study is obtained by averaging the load patterns of several days in the past which are of the same day type as the given day. The effectiveness of the proposed neural network is demonstrated by the short-term load forecasting of the Taiwan Power Company

Published in:

IEE Proceedings C - Generation, Transmission and Distribution  (Volume:138 ,  Issue: 5 )