Forecasting of critical points of the temporary series “electrical power consumption of the industrial sector in Medellín city”, using genetic algorithms

Authors

  • Héctor Tabares Universidad de Antioquia
  • Jesús Hernández Universidad Nacional de Colombia

DOI:

https://doi.org/10.17533/udea.redin.20151

Keywords:

genetic algorithms, artificial neural networks

Abstract

The Genetic Algorithms (GA) are inspired by the Darwin’s principle of the evolution of the species and the genetic. They are probabilistic algorithms that offer an adaptative and parallel search mechanism, based on the principle of survival of the most capable and in the reproduction.

This article presents an introduction to the foundations of the GA. Also we show the software simulator AG_UdeA developed with a didactic purpose for the teaching of the GA. The main contribution consists on the application of the GA to predict the critical points for electrical power consumption in the industrial sector in Medellín city for a period of 24 hours.

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Author Biography

Héctor Tabares, Universidad de Antioquia

Departamento de Ingeniería Eléctrica, Facultad de Ingeniería

References

T. Masters. Practical Neural Networks recipes in C++. San Diego, CA. EE. UU. Ed. Academic Press, Inc. 1993. pp. 21-494.

M. Melanie. An Introduction to Genetic Algorithms. Cambridge, Massachusetts, MIT Press. 1996. p. 10.

J. Hilera, Redes Neuronales Artificiales. Fundamentos, modelos y aplicaciones. Madrid, España. Alfaomega, 2000. pp. 22-408.

B. Martín del Brio. Redes neuronales y sistemas difusos. México D. F., México. AlfaOmega, 2002. pp. 20-312.

X.Yao. “Evolving Artificial Neural Networks. School of Computer Science”. Proceedings IEEE, septiembre de 1999. B15 2TT.

Published

2014-07-31

How to Cite

Tabares, H., & Hernández, J. (2014). Forecasting of critical points of the temporary series “electrical power consumption of the industrial sector in Medellín city”, using genetic algorithms. Revista Facultad De Ingeniería Universidad De Antioquia, (40), 95–105. https://doi.org/10.17533/udea.redin.20151