DocumentCode :
2018428
Title :
Load profile determination with artificial evolution
Author :
Frederic, Kruger ; Wagner, Dietmar ; Collet, Philippe
Author_Institution :
LSIIT - ICube, Univ. de Strasbourg, Illkirch, France
fYear :
2013
fDate :
16-20 June 2013
Firstpage :
1
Lastpage :
6
Abstract :
Load profiles are designed to approximate the average load curve of a certain class of end users. They can be used for commercial purposes as well as for load curve estimation. Load profiles are often very inaccurate as they do not take into account factors such as the type of housing of the end users or the presence of electrical heating. In this paper we present a method to determine accurate load profiles by handling the problem as a blind source separation, solved with a genetic algorithm. Data concerning load curves of 20kV feeders as well as a history of energy consumptions of more than 400,000 end users was provided by “É lectricité de Strasbourg Réseaux”. The load profiles found show considerable improvement in the estimation of 20kV feeder load curves.
Keywords :
genetic algorithms; load forecasting; artificial evolution; average load curve; feeder load curves; genetic algorithm; load profile determination; voltage 20 kV; Blind source separation; Correlation; Estimation; Genetic algorithms; Resistance heating; Shape; Sociology;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
PowerTech (POWERTECH), 2013 IEEE Grenoble
Conference_Location :
Grenoble
Type :
conf
DOI :
10.1109/PTC.2013.6652197
Filename :
6652197
Link To Document :
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