Please use this identifier to cite or link to this item:
|Title:||Daily load forecasting with a fuzzy-input-neural network in an intelligent home|
|Authors:||Ling, S. H.|
Leung, Frank H. F.
Tam, Peter K. S.
Electric power distribution
Feedforward neural networks
|Source:||The 10th IEEE International Conference on Fuzzy Systems : meeting the grand challenge : machines that serve people : The University of Melbourne, Australia, December, 2001, Sunday 2nd to Wednesday 5th, p. 449-452.|
|Abstract:||Daily load forecasting is essential to improve the reliability of the AC power line data network and provide optimal load scheduling in an intelligent home system. In this paper, a fuzzy-input-neural network forecaster model is proposed. This model combines a fuzzy system and a neural network. It can forecast the daily load accurately with respect to different day types under various variables. In this model, the fuzzy system performs a preprocessing for the neural network, so that the computational demand of the neural network can be reduced. Simulation results on a daily load forecasting will be given. Comparing the proposed algorithm with that of a conventional neural network, it can be shown that the proposed algorithm produces more accurate forecasting results.|
|Rights:||© 2001 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.|
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
|Appears in Collections:||EIE Conference Papers & Presentations|
Files in This Item:
|Fuzzy-input-neural network_01.pdf||171.93 kB||Adobe PDF||View/Open|
All items in the PolyU Institutional Repository are protected by copyright, with all rights reserved, unless otherwise indicated. No item in the PolyU IR may be reproduced for commercial or resale purposes.