Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/19844
Title: Computational intelligence techniques for home electric load forecasting and balancing
Authors: Ling, SH
Leung, FHF 
Wong, LK
Lam, HK
Keywords: Genetic algorithms
Load balancing
Neural networks
Short-term load forecasting
Issue Date: 2005
Publisher: Imperial College Press
Source: International journal of computational intelligence and applicationss, 2005, v. 5, no. 3, p. 371-391 How to cite?
Journal: International journal of computational intelligence and applications 
Abstract: The paper presents an electric load balancing system for domestic use. An electric load forecasting system, which is realized by a genetic algorithm-based modified neural network, is employed. On forecasting the home power consumption profile, the load balancing system can adjust the amount of energy stored in battery accordingly, preventing it from reaching certain practical limits. A steady consumption from the AC mains can then be obtained which will benefit both the users and the utility company. An example will be given to illustrate the merits of the forecaster, and its performance on achieving the load balancing.
URI: http://hdl.handle.net/10397/19844
ISSN: 1469-0268
EISSN: 1757-5885
DOI: 10.1142/S1469026805001659
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