Forecasting Electricity Demand for Small Colombian Populations.
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Forecasting Electricity Demand for Small Colombian Populations.
.

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Pronóstico de la demanda de electricidad para pequeñas poblaciones colombianas.AbstractThe socioeconomic and cultural behavior of a population may be reflected in the consumption of electrical energy. Due to the foregoing, researchers and academics have developed models to predict electricity demand in the short, medium and long term. This paper presents an Artificial Neural Network (ANN) for the prediction of daily electricity demand (GWh) in small Colombian populations. The methodology proposed by Kaastra and Boyd is used for the construction, training and validation of the network and the development of the model in the statistical software SPSS. This paper conclude that the predicted values with models constructed with Artificial Neural... Ver más

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Cuaderno activa

2027-8101

2619-5232

Gil Vera, Victor Daniel

TECNOLOGICO DE ANTIOQUIA INSTITUCION UNIVERSITARIA

7

2016-07-19

111

120

Colombia

CUADERNO ACTIVA - 2016

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spelling Forecasting Electricity Demand for Small Colombian Populations.
Forecasting Electricity Demand for Small Colombian Populations.
Pronóstico de la demanda de electricidad para pequeñas poblaciones colombianas.AbstractThe socioeconomic and cultural behavior of a population may be reflected in the consumption of electrical energy. Due to the foregoing, researchers and academics have developed models to predict electricity demand in the short, medium and long term. This paper presents an Artificial Neural Network (ANN) for the prediction of daily electricity demand (GWh) in small Colombian populations. The methodology proposed by Kaastra and Boyd is used for the construction, training and validation of the network and the development of the model in the statistical software SPSS. This paper conclude that the predicted values with models constructed with Artificial Neural Networks (ANN) present a greater degree of approach with the real values of electricity demand (GWh). Also it indicates that the values obtained using models developed with other forecasting techniques (game theory, time series, simulation models, and others) allow to include variables and external factors that are difficult to quantify with simple equations.Keywords: Electricity demand, forecasting models, multi-layer perceptron, artificial neural networks, seasonal time series.Resumen El comportamiento socio económico y cultural de una población puede verse reflejado en el consumo de energía eléctrica. Debido a lo anterior, investigadores y académicos han desarrollado modelos que permitan pronosticar la demanda de la misma en el corto, mediano y largo plazo. Este trabajo presenta una red neuronal artificial (RNA) para el pronóstico de la demanda diaria de electricidad (GWh) en pequeñas poblaciones colombianas. Para la construcción, entrenamiento y validación de la red se empleó la metodología propuesta por Kaastra y Boyd en el software estadístico SPSS. Con el desarrollo de este trabajo se concluye que  los valores pronosticados con modelos construidos con redes neuronales artificiales (RNA), presentan un mayor grado de acercamiento a los valores reales de la demanda de electricidad (GWh), que los valores obtenidos con modelos desarrollados con otras técnicas de pronóstico (teoría de juegos, series de tiempo, modelos de simulación, entre otros), ya que permiten incluir variables y factores externos que son difíciles de cuantificar por medio de simples ecuaciones. Palabras clave: Demanda de electricidad, modelo de pronóstico, perceptron multicapa, redes neuronales artificiales, series de tiempo estacionales.
Gil Vera, Victor Daniel
7
1
Artículo de revista
Journal article
2016-07-19T00:00:00Z
2016-07-19T00:00:00Z
2016-07-19
application/pdf
Tecnológico de Antioquia - Institución Universitaria
Cuaderno activa
2027-8101
2619-5232
https://ojs.tdea.edu.co/index.php/cuadernoactiva/article/view/252
https://ojs.tdea.edu.co/index.php/cuadernoactiva/article/view/252
spa
https://creativecommons.org/licenses/by-nc-sa/4.0/
CUADERNO ACTIVA - 2016
111
120
Aggarwal, S., Saini, L. & Kumar, A. (2009). Electricity Price Forecasting in Deregulated Markets: A Review and Evaluation. International Journal of Electrical Power & Energy Systems, 31(1), 13-22.
Barbosa, L., Kleisinger, G. & Valdez, A. (2001). Utilización del modelo de kohonen y del perceptron multicapa para detectar arritmias cardíacas. Congreo Latinoamericano de Ingeniería Biológica, 5(1), 1-4.
Box, G., Jenkins, G. & Reinsel, G. (2008). Time Series Analysis. New Jersey: Wiley Ed.
Caparrini, F. (2015). Redes neuronales: una visión superficial. Retrieved from http://www.cs.us.es/~fsancho/?e=72
Chandra, P. & Singh, Y. (2004). An Activation Function Adapting Training Algorithm for Sigmoidal Feedforward Networks. Neurocomputing, 61, 429-437.
Crespo, J., Hlouskova, J., Kossmeier, S. & Obersteiner, M. (2004). Forecasting Electricity Spot-Prices Using Linear Univariate Time-Series Models. Applied Energy, 77(1), 87-106.
Guo, D., Zhang, Y., Xiao, Z., Mao, M. & Liu, J. (2015). Common Nature of Learning between BP-Type and Hopfield-Type Neural Networks. Neurocomputing, 167, 578-586.
Kaastra, I. & Boyd, M. (1996). Designing a Neural Network for Forecasting Financial Economic Time Series. Neurocomputing, 10(1), 215-236.
Lázaro, D., Puente, E., Lázaro, M., Capote, J. & Alvear, D. (2013). Posibilidades de un modelo sustituto de incendios mediante el empleo de redes neuronales. Revista Internacional de Métodos Numéricos para Cálculo y Diseño en Ingeniería, 29(3), 129-134.
Moghram, I., & Rahman, S. (1989). Analysis and Evaluation of Five Short-Term Load Forecasting Techniques. IEEE Trans Power Systems, 4(4), 1484-1491.
Pan, J., Liu, X. & Xie, W. (2015). Exponential Stability of a Class of Complex-Valued Neural Networks with Time-Varying Delays. Neurocomputing, 164, 293-299.
Sáenz, N. & Ballesteros, M. (2002). Redes neuronales: concepto, aplicaciones y utilidad en medicina. Atención primaria, 30(2), 119-120.
Singhal, D., & Swarup, K. S. (2011). Electricity Price Forecasting Using Artificial Neural Networks. International Journal of Electrical Power & Energy Systems, 33(3), 550-555.
SPSS. (2007). SPSS Neural Networks. Chicago: SPSS Inc.
Szymczyk, P. (2015). Z-Transform Artificial Neural Networks. Neurocomputing, 168, 1207-1210.
Valverde, R. & Gachet, D. (2007). Identificación de sistemas dinámicos utilizando redes neuronales RBF. Revista iberoamericana de automática e informática industrial RIAI, 4(2), 32-42.
Velásquez, J. D., Zambrano, C. & Vélez, L. (2011). ARNN: Un paquete para la predicción de series de tiempo usando redes neuronales autorregresivas ARNN: A Packages for Time Teries Forecasting Using Autoregressive Neural Networks. Revista avances en sistemas e informática, 8(2).
Wang, P., Li, B., & Li, Y. (2015). Square-Mean Almost Periodic Solutions for Impulsive Stochastic Shunting Inhibitory Cellular Neural Networks with Delays. Neurocomputing, 167, 76-82.
Weron, R., & Misiorek, A. (2014). Electricity Price Forecasting: A Review of the State-of-the-Art with a Look into the Future. International Journal of Forecasting, 30(4), 1030-1081.
X&M Company. (2014). Retrieved from http://xm-company.com/
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title Forecasting Electricity Demand for Small Colombian Populations.
spellingShingle Forecasting Electricity Demand for Small Colombian Populations.
Gil Vera, Victor Daniel
title_short Forecasting Electricity Demand for Small Colombian Populations.
title_full Forecasting Electricity Demand for Small Colombian Populations.
title_fullStr Forecasting Electricity Demand for Small Colombian Populations.
title_full_unstemmed Forecasting Electricity Demand for Small Colombian Populations.
title_sort forecasting electricity demand for small colombian populations.
title_eng Forecasting Electricity Demand for Small Colombian Populations.
description Pronóstico de la demanda de electricidad para pequeñas poblaciones colombianas.AbstractThe socioeconomic and cultural behavior of a population may be reflected in the consumption of electrical energy. Due to the foregoing, researchers and academics have developed models to predict electricity demand in the short, medium and long term. This paper presents an Artificial Neural Network (ANN) for the prediction of daily electricity demand (GWh) in small Colombian populations. The methodology proposed by Kaastra and Boyd is used for the construction, training and validation of the network and the development of the model in the statistical software SPSS. This paper conclude that the predicted values with models constructed with Artificial Neural Networks (ANN) present a greater degree of approach with the real values of electricity demand (GWh). Also it indicates that the values obtained using models developed with other forecasting techniques (game theory, time series, simulation models, and others) allow to include variables and external factors that are difficult to quantify with simple equations.Keywords: Electricity demand, forecasting models, multi-layer perceptron, artificial neural networks, seasonal time series.Resumen El comportamiento socio económico y cultural de una población puede verse reflejado en el consumo de energía eléctrica. Debido a lo anterior, investigadores y académicos han desarrollado modelos que permitan pronosticar la demanda de la misma en el corto, mediano y largo plazo. Este trabajo presenta una red neuronal artificial (RNA) para el pronóstico de la demanda diaria de electricidad (GWh) en pequeñas poblaciones colombianas. Para la construcción, entrenamiento y validación de la red se empleó la metodología propuesta por Kaastra y Boyd en el software estadístico SPSS. Con el desarrollo de este trabajo se concluye que  los valores pronosticados con modelos construidos con redes neuronales artificiales (RNA), presentan un mayor grado de acercamiento a los valores reales de la demanda de electricidad (GWh), que los valores obtenidos con modelos desarrollados con otras técnicas de pronóstico (teoría de juegos, series de tiempo, modelos de simulación, entre otros), ya que permiten incluir variables y factores externos que son difíciles de cuantificar por medio de simples ecuaciones. Palabras clave: Demanda de electricidad, modelo de pronóstico, perceptron multicapa, redes neuronales artificiales, series de tiempo estacionales.
author Gil Vera, Victor Daniel
author_facet Gil Vera, Victor Daniel
citationvolume 7
citationissue 1
publisher Tecnológico de Antioquia - Institución Universitaria
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references Aggarwal, S., Saini, L. & Kumar, A. (2009). Electricity Price Forecasting in Deregulated Markets: A Review and Evaluation. International Journal of Electrical Power & Energy Systems, 31(1), 13-22.
Barbosa, L., Kleisinger, G. & Valdez, A. (2001). Utilización del modelo de kohonen y del perceptron multicapa para detectar arritmias cardíacas. Congreo Latinoamericano de Ingeniería Biológica, 5(1), 1-4.
Box, G., Jenkins, G. & Reinsel, G. (2008). Time Series Analysis. New Jersey: Wiley Ed.
Caparrini, F. (2015). Redes neuronales: una visión superficial. Retrieved from http://www.cs.us.es/~fsancho/?e=72
Chandra, P. & Singh, Y. (2004). An Activation Function Adapting Training Algorithm for Sigmoidal Feedforward Networks. Neurocomputing, 61, 429-437.
Crespo, J., Hlouskova, J., Kossmeier, S. & Obersteiner, M. (2004). Forecasting Electricity Spot-Prices Using Linear Univariate Time-Series Models. Applied Energy, 77(1), 87-106.
Guo, D., Zhang, Y., Xiao, Z., Mao, M. & Liu, J. (2015). Common Nature of Learning between BP-Type and Hopfield-Type Neural Networks. Neurocomputing, 167, 578-586.
Kaastra, I. & Boyd, M. (1996). Designing a Neural Network for Forecasting Financial Economic Time Series. Neurocomputing, 10(1), 215-236.
Lázaro, D., Puente, E., Lázaro, M., Capote, J. & Alvear, D. (2013). Posibilidades de un modelo sustituto de incendios mediante el empleo de redes neuronales. Revista Internacional de Métodos Numéricos para Cálculo y Diseño en Ingeniería, 29(3), 129-134.
Moghram, I., & Rahman, S. (1989). Analysis and Evaluation of Five Short-Term Load Forecasting Techniques. IEEE Trans Power Systems, 4(4), 1484-1491.
Pan, J., Liu, X. & Xie, W. (2015). Exponential Stability of a Class of Complex-Valued Neural Networks with Time-Varying Delays. Neurocomputing, 164, 293-299.
Sáenz, N. & Ballesteros, M. (2002). Redes neuronales: concepto, aplicaciones y utilidad en medicina. Atención primaria, 30(2), 119-120.
Singhal, D., & Swarup, K. S. (2011). Electricity Price Forecasting Using Artificial Neural Networks. International Journal of Electrical Power & Energy Systems, 33(3), 550-555.
SPSS. (2007). SPSS Neural Networks. Chicago: SPSS Inc.
Szymczyk, P. (2015). Z-Transform Artificial Neural Networks. Neurocomputing, 168, 1207-1210.
Valverde, R. & Gachet, D. (2007). Identificación de sistemas dinámicos utilizando redes neuronales RBF. Revista iberoamericana de automática e informática industrial RIAI, 4(2), 32-42.
Velásquez, J. D., Zambrano, C. & Vélez, L. (2011). ARNN: Un paquete para la predicción de series de tiempo usando redes neuronales autorregresivas ARNN: A Packages for Time Teries Forecasting Using Autoregressive Neural Networks. Revista avances en sistemas e informática, 8(2).
Wang, P., Li, B., & Li, Y. (2015). Square-Mean Almost Periodic Solutions for Impulsive Stochastic Shunting Inhibitory Cellular Neural Networks with Delays. Neurocomputing, 167, 76-82.
Weron, R., & Misiorek, A. (2014). Electricity Price Forecasting: A Review of the State-of-the-Art with a Look into the Future. International Journal of Forecasting, 30(4), 1030-1081.
X&M Company. (2014). Retrieved from http://xm-company.com/
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