Characterization and identification of electrical customers through the use of self-organizing maps and daily load parameters
Author
Valero Verdú, Sergio; Ortiz García, Mario; García Franco, Francisco J.; Encinas Redondo, Nuria; Gabaldón Marín, Antonio; [et al.]Knowledge Area
Ingeniería EléctricaPublication date
2004-10Publisher
IEEE Power & Energy Society (PES)Bibliographic Citation
VALERO VERDÚ, Sergio et al. Characterization and identification of electrical customers through the use of self-organizing maps and daily load parameters. En: Power Systems Conference and Exposition (2004: New York). IEEE PES. Power Systems Conference & Exposition. 10-13 October, 2004. New York: IEEE Power & Energy Society, 2004. vol. 2. Pp. 899- 906.Keywords
Redes neuronales artificialesSegmentación de clientes eléctricos
Mercado eléctrico
Análisis tiempo-frecuencia
Agregación de cliente
Artificial neural networks
Electrical customer segmentation
Electricity market
Time-Frequency analysis
Abstract
This paper shows the capacity of modern
computational techniques such as the self-organizing map (SOM)
as a methodology to achieve the classification of the electrical
customers in a commercial or geographical area. This approach
allows to extract the pattern of customer behavior from historic
load demand series. Several ways of data analysis from load
curves can be used to get different input data to “feed” the neural
network. In this work, we propose two methods to improve
customer clustering: the use of frequency-based indices and the
use of the hourly load curve. Results of a case study developed on
a set of different spanish customers and a comparison between
the two approachs proposed here are presented.
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