Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1409
Title: A neural fuzzy network with optimal number of rules for short-term load forecasting in an intelligent home
Authors: Ling, S. H.
Lam, H. K.
Leung, Frank H. F.
Tam, Peter K. S.
Subjects: Computer simulation
Digital arithmetic
Electric appliances
Electric load forecasting
Electric power transmission networks
Genetic algorithms
Membership functions
Neural networks
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. 1456-1459.
Abstract: In this paper, a short-term home daily load forecasting realized by a neural fuzzy network (NFN) and an improved genetic algorithm (GA) is proposed. It can forecast the daily load accurately with respect to different day types and weather information. It will also be shown that the improved GA performs better than the traditional GA on some benchmark test functions. By introducing switches in the links of the neural fuzzy network, the optimal network structure can be found by the improved GA. The membership functions and the number of rules of the neural fuzzy network can be generated automatically. Simulation results for a short-term daily load forecasting in an intelligent home will be given.
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Type: Conference Paper
URI: http://hdl.handle.net/10397/1409
ISBN: 0-7803-7293-X
Appears in Collections:EIE Conference Papers & Presentations

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