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Title: Short-term daily load forecasting in an intelligent home with GA-based neural network
Authors: Ling, SH
Leung, FHF 
Lam, HK
Tam, PKS
Issue Date: 2002
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.
Keywords: Computer simulation
Electric load forecasting
Error analysis
Genetic algorithms
Intelligent agents
Neural networks
Publisher: IEEE
ISBN: 0-7803-7278-6
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.
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