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 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.

• 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
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/