Electric Load Forecasting with Genetic Neural Networks

  • F. J. Marín
  • F. Sandoval
Conference paper


This paper presents an evolution algorithm for optimizing a neural network architecture. The procedure establishes the structure and the training algorithm, as well as searching the minimal topology of the network, eliminating neurons and interconnection weights. The model network, this is, feedforward or feedback, can be selected by the user. This methodology is applied to the real problem of the forecasting in power system load in the city of Málaga (Spain) between the years 1992 and 1993. The results produced by the evolution algorithm are tested with a statistical regression analysis and with other training algorithms of paradigms of neural networks.


Hide Layer Optimal Topology Hide Neuron Neural Network Architecture Load Forecast 
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  1. [1]
    D.E. Goldberg. Genetic Algorthims in Search Optimization and Machine Learning. Addison Wesley, 1989.Google Scholar
  2. [2]
    F. J. Marin, J. Ruano, and F. Sandoval. Optimization of artificial neural networks using a cooperative-coevolutive genetic algorithm. Technical report, University of Málaga, 1996.Google Scholar
  3. [3]
    T. M. Peng, N. F. Hubele, and G. G. Karady. Advancement in the application of neural networks for short term load forecasting. IEEE Trans. PWRS, 7(1), 1992.Google Scholar
  4. [4]
    M. A. Potter and K. A. DeJong. A cooperative coevolutionary approach to function optimization. In Y. Davidor, H-P. Schwefel, and R. Manner, editors, Parallel Problem Solving from Nature III. Springer-Verlag, 1994.Google Scholar
  5. [5]
    H. K. Temraz and V. H. Quintana. Applications of the decomposition technique for forecasting the load of a large electric power network. IEE Proceedings Hener. Transm. Distrib., 143(1), 1996.Google Scholar

Copyright information

© Springer-Verlag Wien 1998

Authors and Affiliations

  • F. J. Marín
    • 1
  • F. Sandoval
    • 2
  1. 1.Dpto. ElectrónicaUniversidad de Málaga, Campus TeatinosMálagaSpain
  2. 2.Dpto. Tecnología ElectrónicaUniversidad de MálagaMálagaSpain

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