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, S. H.
Leung, Frank H. F.
Tam, Peter K. S.
Subjects: 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.
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.
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Type: Conference Paper
URI: http://hdl.handle.net/10397/1373
ISBN: 0-7803-7293-X
Appears in Collections:EIE Conference Papers & Presentations

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