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Short-Term Load Forecasting using Cartesian Genetic Programming: an Efficient Evolutive Strategy Case: Australian electricity market

Autor
Giacometto, Francisco javier; Kampouropoulos, K.; Romeral, L.
Tipus d'activitat
Presentació treball a congrés
Nom de l'edició
41st Annual Conference of the IEEE Industrial Electronics Society
Any de l'edició
2015
Data de presentació
2015-11-09
Llibre d'actes
Proceedings of 41th Annual Conference on IEEE Industrial Electronics Society (IECON 2015)
Pàgina inicial
5087
Pàgina final
5094
DOI
https://doi.org/10.1109/IECON.2015.7392898 Obrir en finestra nova
Repositori
http://hdl.handle.net/2117/85741 Obrir en finestra nova
Resum
Currently, the Cartesian Genetic Programming approaches applied to regression problems tackle the evolutive strategy from a static point of view. They are confident on the evolving capacity of the genetic algorithm, with less attention being paid over alternative methods to enhance the generalization error of the trained models or the convergence time of the algorithm. On this article, we propose a novel efficient ...
Citació
Giacometto, F., Kampouropoulos, K., Romeral, J. Short-Term Load Forecasting using Cartesian Genetic Programming: an Efficient Evolutive Strategy Case: Australian electricity market. A: Annual Conference of the IEEE Industrial Electronics Society. "Proceedings of 41th Annual Conference on IEEE Industrial Electronics Society (IECON 2015)". Yokohama: 2015, p. 5087-5094.
Paraules clau
cartesian genetic programming, convergence time, evolutive strategy, generalization error, short-term load forecast
Grup de recerca
MCIA - Motion Control and Industrial Applications Research Group
PERC-UPC - Centre de Recerca d'Electrònica de Potència UPC

Participants

  • Giacometto Torres, Francisco Javier  (autor ponent)
  • Kampouropoulos, Konstantinos  (autor ponent)
  • Romeral Martinez, Jose Luis  (autor ponent)