Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1373
Title: Daily load forecasting with a fuzzy-input-neural network in an intelligent home
Authors: Ling, SH
Leung, FHF 
Tam, PKS
Keywords: Algorithms
Backpropagation
Computer simulation
Electric power distribution
Feedforward neural networks
Fuzzy sets
Intelligent buildings
Membership functions
Transfer functions
Issue Date: 2001
Publisher: IEEE
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 How to cite?
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.
URI: http://hdl.handle.net/10397/1373
ISBN: 0-7803-7293-X
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.
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