Predicción de la demanda de energía eléctrica basado en análisis Wavelet y un modelo neuronal auto-regresivo no lineal Nar

En este artículo se presenta una propuesta metodológica para la predicción mensual de energía eléctrica del Sistema Interconectado Nacional (SIN) de Colombia, mediante la transformada discreta de Wavelet y una red neuronal artificial. El modelo propuesto utiliza como punto de partida una base de datos univariada en miles de Gwh por mes, entre Agosto del 1995 y Junio de 2010, disponible en el sistema de Neón (www.xm.com.co). Esta serie es denominada original y consta de 179 muestras.  Con el fin de extraer tendencia y estacionalidad de la serie, en la etapa de pre-procesamiento  se utilizó  la transformada discreta wavelet (DWT). Debido al carácter no lineal que presenta la serie original, se manejó un modelo neuronal autorregresivo no lineal (NAR) y se  determinó un vector de las entradas pasadas necesarias para la predicción con el autocorrelograma (relación que tiene el valor actual de la serie original con sus valores pasados) de la serie residual. Los resultados obtenidos fueron contrastados con un modelo estadístico lineal autorregresivo (AR)

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

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 predictionEn 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

Geometric voice : interaction of the people with visually impaired with the treatment of geometric figures and their tactile visualization through a braille printer

Orientador: Luiz César MartiniDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Este trabalho apresenta os resultados obtidos no desenvolvimento de figuras geométricas em duas (2D) e três dimensões (3D), realizadas por um programa inédito desenvolvido nesta pesquisa e destinado principalmente a pessoas com deficiência visual. O programa permite a acessibilidade para criar e imprimir desenhos geométricos, funções que até o momento não estavam disponíveis para pessoas que não enxergam. O aplicativo trata o projeto e criação de formas geométricas a partir dos parâmetros próprios utilizando um sintetizador de voz e assistentes ou me-nus especiais orientados a usuários com deficiência visual. Além disso, o aplicativo facilita a impressão das formas geométricas na mesma folha tanto em Braille, como em relevos, além de uma impressão comum em tinta destinada às pessoas que conseguem enxergarAbstract: This work presents the results obtained in the development of geometric figures in two (2D) and three dimensions (3D), performed by a novel program developed primarily for people with visual impairments. The program allows accessibility to create and print geometric designs by itself, functionalities to date not available for visually impaired users. The program treats the projection and creation of geometric shapes from its own parameters using a voice synthesizer and special menus oriented to people with visually impaired people. Moreover, the application facilitates the joint printing of geometrical shapes on the same sheet us Braille, reliefs, and inkMestradoEngenharia de ComputaçãoMestre em Engenharia Elétric

Tools for Teaching Mathematical Functions and Geometric Figures to Tactile Visualization through a Braille Printer for Visual Impairment People

In this article, we showed the features and facilities offered by two new computer programs developed for the treatment and generation of geometric figures and math functions, through a Braille printer designed for visually impaired people. The programs have complete accessible features, in which users with full visual impairments can communicate with the systems via short-keys, and the speech synthesizer. The system sends sound messages that will accompanying the user during all the process to generate geometrical figures or to do a mathematical treatment. Finally, a tactile visualization displays as the results to the person with visual impairment, thus they will can complete their geometry and mathematical studies

Predicción de la demanda de energía eléctrica basado en análisis Wavelet y un modelo neuronal auto-regresivo no lineal Nar

En este artículo se presenta una propuesta metodológica para la predicción mensual de energía eléctrica del Sistema Interconectado Nacional (SIN) de Colombia, mediante la transformada discreta de Wavelet y una red neuronal artificial. El modelo propuesto utiliza como punto de partida una base de datos univariada en miles de Gwh por mes, entre Agosto del 1995 y Junio de 2010, disponible en el sistema de Neón (www.xm.com.co). Esta serie es denominada original y consta de 179 muestras.  Con el fin de extraer tendencia y estacionalidad de la serie, en la etapa de pre-procesamiento  se utilizó  la transformada discreta wavelet (DWT). Debido al carácter no lineal que presenta la serie original, se manejó un modelo neuronal autorregresivo no lineal (NAR) y se  determinó un vector de las entradas pasadas necesarias para la predicción con el autocorrelograma (relación que tiene el valor actual de la serie original con sus valores pasados) de la serie residual. Los resultados obtenidos fueron contrastados con un modelo estadístico lineal autorregresivo (AR)

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

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

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

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.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 predictio

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

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