Control del sobreajuste en redes neuronales tipo cascada correlación aplicado a la predicción de precios de contratos de electricidad

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 d...

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Autores:
Villa G, Fernán A; Universidad Nacional de Colombia
Velásquez H, Juan D; Universidad Nacional de Colombia
Sánchez S, Paola A; Universidad Simón Bolívar
Tipo de recurso:
Article of journal
Fecha de publicación:
2015
Institución:
Universidad de Medellín
Repositorio:
Repositorio UDEM
Idioma:
spa
OAI Identifier:
oai:repository.udem.edu.co:11407/1811
Acceso en línea:
http://hdl.handle.net/11407/1811
Palabra clave:
time series forecast
cascade correlation
neural networks
electricity market of Colombia
pronóstico de series de tiempo
redes cascada correlación
rede neuronales
mercado de electricidad colombiano
Rights
License
http://creativecommons.org/licenses/by-nc-sa/4.0/
Description
Summary: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.