DOI:
https://doi.org/10.14483/23448393.3836Publicado:
2011-12-18Número:
Vol. 16 Núm. 2 (2011): Julio - DiciembreSección:
ArtículosMétodo 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
Palabras clave:
predicción de carga eléctrica, modelo neuronal no lineal autoregresivo, predicción en series de tiempo, análisis con transformada wavelet (es).Palabras clave:
electric load forecasting, nonlinear autoregressive neural model, time series forecasting, wavelet transform analysis. (en).Descargas
Resumen (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)
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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