Cart (Loading....) | Create Account
Close category search window
 

Design of artificial neural networks for short-term load forecasting. II. Multilayer feedforward networks for peak load and valley 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
$31 $31
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)
Hsu, Yuan-Yih ; Dept. of Electr. Eng., Nat. Taiwan Univ., Taipei, Taiwan ; Chien-Chuen Yang

For pt.I see ibid., vol.138, no.5, p.407-13 (1991). In part I of the paper, a neural network with unsupervised learning was proposed to identify the day types and compute the hourly load pattern by averaging the load patterns of the same day type. In this part of the paper a neural network, commonly referred to as the multilayer feedforward network, is developed to forecast daily peak load and valley load. Unlike the self-organising feature maps in part I, the multilayer feedforward network is a neural net with supervised learning. The neural net is first trained using historical weather and load data. Then the trained neural net is applied to predict daily peak load and valley load. These peak and valley loads, when combined with the hourly load pattern, can yield the desired hourly loads. Results from short-term load forecasting of the Taiwan power system are given to demonstrate the effectiveness of the proposed neural networks

Published in:

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

Date of Publication:

Sep 1991

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.