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Resumen

 

ARTIFICIAL INTELLIGENCE IN FORECASTING DEMANDS FOR ELECTRICITY: AN APPLICATION IN OPTIMIZATION OF ENERGY RESOURCES

INTELIGENCIA ARTIFICIAL EN PRONOSTICO DE DEMANDA DE ENERGIA ELECTRICA: UNA APLICACION EN OPTIMIZACION DE RECURSOS ENERGETICOS
Ing. Henry Omar Sarmiento Maldonado.
Ing. Walter Mauricio Villa Acevedo.
Docente Politécnico Colombiano Jaime Isaza Cadavid.

Abstract: This paper presents a current application of neural networks in the task of forecasting electricity demands in Colombia. They are used networks of the Multi Layer Perceptron (MLP) type with backpropagation training algorithms, and Radial Basic Function (RBF). The information available with time and hourly values of demands in megawatts (MW) is organized in such a way that the task of forecasting raises a problem of classifying information where previous networks have shown good performance. The task of organizing information, training, validation and prognosis are developed with Matlab programming.

Resumen: En este artículo se presenta una aplicación actual de las redes neuronales en la tarea de pronóstico de demanda eléctrica en Colombia. Se utilizan redes del tipo Multi Layer Perceptron (MLP) con algoritmos de entrenamiento Backpropagation, y Radial Basic Function (RBF). La información disponible de tiempo y valor de demanda horaria en megavatios (MW) es organizada de tal forma que se plantea la tarea de pronóstico como un problema de clasificación de información donde las anteriores redes han evidenciado un buen desempeño. La tarea de organización de la información, entrenamiento, validación y pronóstico son desarrolladas con programación en Matlab.

Keywords: Neural networks, forecasts, feedforward networks, artificial intelligence, electric power systems.