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Enhancing optimal transmission or subtransmission planning by using decision trees.

J. Peco, E.F. Sánchez-Úbeda, T. Gómez

Due to the large size of electric power systems there is a very high computational burden when obtaining the optimum network by using classical optimization techniques. Several authors have used heuristics and/or sensisitivities in order to guide the search of optimal network investments. This paper proposes an Automatic Learning approach in order to decide whether a network change will improve the overall costs or not. more specifically, Decision Trees methods are used to identifiy a set of simple and reliable rules which combine criteria trees are integrated in a subtransmission planning tool, improving dramatically both the ?optimality? of the resultant network and the computational time.


Palabras clave: Transmission planning, planning rules, automatic learning, decision trees, genetic algorithms, data mining.

IEEE Power Tech '99 Conference, Paper BPT99-304-16, ISBN: 0780358376, Budapest (Hungría). 28 Agosto - 02 Septiembre 1999

DOI: DOI icon 10.1109/PTC.1999.826607    

Fecha de publicación: agosto 1999.



Cita:
J. Peco, E.F. Sánchez-Úbeda, T. Gómez, Enhancing optimal transmission or subtransmission planning by using decision trees, IEEE Power Tech '99 Conference, Paper BPT99-304-16, ISBN: 0780358376. ISBN: 0-7803-5836-8, Budapest, Hungría, 28 Agosto - 02 Septiembre 1999


    Líneas de investigación:
  • *Predicción y Análisis de Datos
  • *Planificación táctica a medio plazo

IIT-99-059A

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