Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1407
Title: Short-term daily load forecasting in an intelligent home with GA-based neural network
Authors: Ling, S. H.
Leung, Frank H. F.
Lam, H. K.
Tam, Peter K. S.
Subjects: Computer simulation
Electric load forecasting
Error analysis
Genetic algorithms
Intelligent agents
Neural networks
Optimization
Issue Date: 2002
Publisher: IEEE
Source: IJCNN'02 : proceedings of the 2002 International Joint Conference on Neural Networks : May 12-17, 2002, Honolulu, Hawaii, p. 997-1001.
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 short-term daily load forecasting realized by a GA-based neural network is proposed. A neural network with a switch introduced to each link is employed to minimize forecasting errors and forecast the daily load with respect to different day types and weather information. Genetic algorithm (GA) with arithmetic crossover and non-uniform mutation is used to learn the input-output relationships of an application and the optimal network structure. Simulation results on a short-term daily load forecasting in an intelligent home will be given.
Rights: © 2002 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.
Type: Conference Paper
URI: http://hdl.handle.net/10397/1407
ISBN: 0-7803-7278-6
Appears in Collections:EIE Conference Papers & Presentations

Files in This Item:
File Description SizeFormat 
GA-based neural network_02.pdf252.12 kBAdobe PDFView/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.