Método para la Predicción de Demanda Mensual de Electricidad en Colombia utilizando Análisis Wavelet y Modelos Auto-regresivos No Lineales

A Method for the Monthly Electricity Demand Forecasting in Colombia based on Wavelet Analysis and a Nonlinear Autoregressive Model

  • Cristhian Moreno-Chaparro Universidad Distrital Francisco José de Caldas
  • Jeison Salcedo-Lagos Universidad Distrital Francisco José de Caldas
  • Edwin Rivas Trujillo Universidad Distrital Francisco José de Caldas
  • Alvaro Orjuela Canon Universidad Distrital Francisco José de Caldas
Palabras clave: electric load forecasting, nonlinear autoregressive neural model, time series forecasting, wavelet transform analysis. (en_US)
Palabras clave: predicción de carga eléctrica, modelo neuronal no lineal autoregresivo, predicción en series de tiempo, análisis con transformada wavelet (es_ES)

Resumen (es_ES)

En este artículo se propone un método para la predicción mensual de la demanda en el Sistema Interconectado Nacional Eléctrico de Colombia. El método realiza preprocesamiento de la serie de tiempo utilizando un análisis multiresolución mediante tranformada wavelet discreta; se presenta un estudio para la selección de la wavelet madre y su orden, asi como del nivel de descomposición. Dado que originalmente la serie tiene comportamiento no lineal, se utilizó igualmente un modelo no lineal autoregresivo. La predicción se obtiene añadiendo a la tendencia, el estimado obtenido con el residual de la serie combinado con otros componentes extraídos durante el preproceamiento.

Se incluye una revisión bibliográfica de investigaciones realizadas internacionalmente y en Colombia en relación a la aplicación de la transformada wavelet y el modelo autoregresivo no lineal a la predicción de energía eléctrica.

Resumen (en_US)

This paper proposes a monthly electricity forecast method for the National Interconnected System (SIN) of Colombia. The method preprocesses the time series using a Multiresolution Analysis (MRA) with Discrete Wavelet Transform (DWT); a study for the selection of the mother wavelet and her order, as well as the level decomposition was carried out. Given that original series follows a non-linear behaviour, a neural nonlinear autoregressive (NAR) model was used. The prediction was obtained by adding the forecast trend with the estimated obtained by the residual series combined with further components extracted from preprocessing.

A bibliographic review of studies conducted internationally and in Colombia is included, in addition to references to investigations made with wavelet transform applied to electric energy prediction and studies reporting the use of NAR in prediction

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Biografía del autor/a

Cristhian Moreno-Chaparro, Universidad Distrital Francisco José de Caldas

Electronic engineer from "Francisco José de Caldas"District University, Bogotá, Colombia. He is currently a Master Student Electrical and Computer Engineer of the “University of CampinasUNICAMP”, Campinas - São Paulo, Brazil. His interests are in the areas of electricity demand forecast, wavelet analysis and computational Intelligence.

Jeison Salcedo-Lagos, Universidad Distrital Francisco José de Caldas

Electronic engineer from "Francisco José de Caldas"District University, Bogotá, Colombia. He is currently a Member of research group in interference and Electromagnetic Compatibility GCEM – UD. His interests are in the areas of electricity demand forecast, wavelet analysis and computational Intelligence and neural networks.

Edwin Rivas Trujillo, Universidad Distrital Francisco José de Caldas

Ph.D. from “Carlos III de Madrid” University, Madrid, Spain in 2009. Heis currently an Associate Professor at “Francisco Jose de Caldas” District University, Bogotá, Colombia. Member of research group in interference and ElectromagneticCompatibility GCEM – UD. His interests are in the areas of power systems, Smart grid and signal processing, as applied to power transformer condition monitoring and vibration signature analysis applied to machinery.

Alvaro Orjuela Canon, Universidad Distrital Francisco José de Caldas

Msc. from“Universidade Federal Do Rio De Janeiro”, Rio de Janeiro, Brazil in 2010. He is currently an Associate Professor at “Francisco Jose de Caldas” District University, Bogotá, Colombia.His interests are in the areas of Digital Signal Processing, computational Intelligence and Computational Intelligence in Health.

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Cómo citar
Moreno-Chaparro, C., Salcedo-Lagos, J., Rivas Trujillo, E., & Orjuela Canon, A. (2011). Método para la Predicción de Demanda Mensual de Electricidad en Colombia utilizando Análisis Wavelet y Modelos Auto-regresivos No Lineales. Ingeniería, 16(2), 94-106. https://doi.org/10.14483/23448393.3836
Revista INGENIERÍA, Vol. 16, No. 2, 2011
Publicado: 2011-12-18

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