Overfitting control inside cascade correlation neural networks applied to electricity contract price prediction

Authors

  • Fernán A Villa G Universidad Nacional de Colombia
  • Juan D Velásquez H Universidad Nacional de Colombia
  • Paola A Sánchez S Universidad Simón Bolívar

DOI:

https://doi.org/10.22395/rium.v14n26a10

Keywords:

time series forecast, cascade correlation, neural networks, electricity market of Colombia

Abstract

Prediction of electricity prices is considered a difficult task due to the number and complexity of factors that influence their performance, and their relationships. Neural networks cascade correlation - CASCOR allows to do a constructive learning and it captures better the characteristics of the data; however, it has a high tendency to overfitting. To control overfitting in some areas regularization techniques are used. However, in the literature there are no studies that: i) use regularization techniques to control overfitting in CASCOR networks, ii) use CASCOR networks in predicting of electrical series iii) compare the performance with tra­ditional neural networks or statistical models. The aim of this paper is to model and predict the behavior of the price series of electricity contracts in Colombia, using CASCOR networks and controlling the overfitting by regularization techniques

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Author Biographies

Fernán A Villa G, Universidad Nacional de Colombia

Docente-Investigador Universidad Nacional de Colombia

Juan D Velásquez H, Universidad Nacional de Colombia

Universidad Nacional de Colombia

Paola A Sánchez S, Universidad Simón Bolívar

Docente Investigador Universidad Simón Bolívar

How to Cite

Villa G, F. A., Velásquez H, J. D., & Sánchez S, P. A. (2015). Overfitting control inside cascade correlation neural networks applied to electricity contract price prediction. Revista Ingenierías Universidad De Medellín, 14(26), 161–176. https://doi.org/10.22395/rium.v14n26a10

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Section

Articles