Análisis de Consumo de Energía Eléctrica Usando Análisis de Componentes Independientes

Carlos Boya

Resumen


Este documento presenta un método para el análisis de la serie de tiempo de consumo eléctrico utilizando Análisis de Componentes Independientes (ICA). Con este método es posible detectar y extraer de manera automática factores que influyen separadamente en el consumo eléctrico, así como estudiar su relación en la curva de consumo diario con ayuda de perfiles estimados paralelamente con ICA. ICA logra aislar estos comportamientos en un grupo de componentes sin perder representatividad de los datos ofreciendo una visión más profunda del consumo eléctrico con el objetivo de facilitar su modelado y pronóstico.

Palabras clave


Análisis de componentes independientes, análisis de series de tiempo, consumo de energía eléctrica.

Texto completo:

PDF HTML

Referencias


(1) R. Sanchez, X. Guillaud, G. Dauphin-Tanguy, “Hybrid electrical

power system modeling and management”, Simulation

Modelling Practice and Theory, Vol. 25, pp. 190–205, Jun. 2012.

(2) H. S. Hippert, C.E Pedreira, R.C. Souza, “Neural networks for

short- term load forecasting: a review and evaluation”, I E E E

Transaction on Power Systems, Vol. 16, no. 1, pp. 44-55, Feb. 2001.

(3) S.M Al-Alawi, S.M. Islam, “Principles of electricity demand

forecasting Part1. Methodologies”, Power Engineering Journal,

Vol. 10, no. 3, pp. 139-143, Jun. 1996.

(4) B. L. Bowerman, R. T. O’Connell, A. B. Koehler, Pronósticos,

Series de Tiempo y Regresión, Un Enfoque Aplicado, Cengage

Learning Editores , 2009.

(5) P. J. Brockwell, R. A. Davis, Introduction to Series and Forecasting,

Springer-Verlag, New York, USA, 2002.

(6) S.S. Papas, L. Ekomomou, D.Ch. Karamousantas, G.E.

Chatzarakis, S.K. Katsikas, P. Liatsis, "Electricity demand loads

modeling using AutoRegressive Moving Average (ARMA)

models", Energy, Vol. 33, no. 9, pp. 1353-1360, Sept. 2008.

(7) J.W. Taylor, "Short-term load forecasting with exponentially

Weighted methods ", IEEE transactions on Power Systems, Vol.

, no. 1, pp. 458-640, Feb. 2012.

(8) A.D. Back, A.S. Weigend, “A 2rst application of independent

component analysis to extracting structure from stock returns”,

International journal of neural systems, Vol. 8, no. 4, pp. 473-484,

Aug. 1997.

(9) K. Kivilouto, E. Oja, “Independent component analysis for

parallel 2nancial time series”, In Proc. Int. Conf. on Neural

Information Processing (ICONIP’98), Tokyo, Japan, October,

(10) S. Malaroiu, K. Kivilouto and E. Oja, “Time series prediction

with independent component analysis”, In Proc. Int. Conf. on

Advanced Investment Technology, Gold Coast, Autralia, October,

(11) E. Oja, et al., “Independent component analysis for 2nancial time

series”, Adaptive Systems for Signal Processing,

Communications, and Control Symposium 2000, Alberta, Canada,

Oct. 2000.

(12) P. Comon, “Independent component analysis, A new concept?”,

Signal processing, Vol. 36, no. 3, pp. 287-314, April 1994.

(13) J. Escudero, et al., "Artifact Removal in Magnetoencephalogram

Background Activity With Independent Component Analysis",

IEEE Transactions on Biomedical Engineering, Vol. 54, no. 11, pp.

-1973, 2007.

(14) W. Nakamura, et al., "Removal of ballistocardiogram artifacts

from simultaneously recorded EEG and fMRI data using

independent component analysis", IEEE Transactions on

Biomedical Engineering, Vol. 53, no. 7, pp. 1294-1308, 2006.

(15) P. Pertilä, "Online blind speech separation using multiple

acoustic speaker tracking and time–frequency masking”,

Computer Speech &Language, Vol. 27, pp. 683-702, May 2013.

(16) J. Gao, et al., "Independent component analysis for multipleinput

multiple-output wireless communication systems", Signal

Processing, Vol. 91, no. 4, pp. 607-623, April 2011.

(17) T. Ju, et al., "Blind Source Separation of Mixed PD Signals

Produced by Multiple Insulation Defects in GIS," IEEE

Transactions on Power Delivery, Vol. 25, no. 3, pp. 170-176, Jan.

(18) C.H. Chen, “The use of independent component analysis

as a tool for data mining”, Geoscience and remote sensing

symposium, Toronto, Canada, 24-28 June, 2002.

(19) C. J. Lu, et. al., “Financial time series forecasting using

independent component analysis and support vector

regression”, Decision Support Systems, Vol. 47, no. 2, pp. 115-

, May 2009.

(20) A. Hyvarinen, “Fast and Robust Fixed-Point Algorithms for

Independent Component Analysis”, IEEE Transactions on

Neural Networks, Vol. 10, no. 3, pp. 626-634, May 1999.

(21) ICA y BSS group, “The FastICA software package”, [online]:

“http://research.ics.aalto.fi/ica/fastica/. Ultimo acceso:

de mayo de 2014.

(22) A. Hyvarinen, et. al., Independent component analysis:

algorithms and applications, Wiley, New York, USA, 2001.

(23) M.A.A. Lima, A.S. Cerqueira, D.V. Coury, C.A. Duque, “A novel

method for power quality multiple disturbance decomposition

based on Independent Component Analysis”, International

Journal of Electrical Power & Energy Systems, Vol. 42, no. 1, pp.

-604, Nov. 2012.

(24) B. Mijovic, M.D. Vos, I. Gligorijevic, J. Taelman, S.V. Huffel,

“Source Separation From Single-Channel Recordings by

Combining Empirical-Mode Decomposition and Independent

Component Analysis”, IEEE transactions on biomedical

engineering, Vol. 57, no. 9, pp. 2188-2196, Sept. 2010.

(25) ETESA, “Comportamiento del sistema”, Centro de Nacional

de despacho [online], Disponible: http://www.cnd.com.pa/

informes.php?cat=5, Accesado: 2 de Junio de 2014.






Copyright (c) 2016 I+D Tecnológico



Indexado y Catalogado en:

DRJI Indexed Journal





© 2016 Portal de Revistas de la Universidad Tecnológica de Panamá
Este sitio es un componente del proyecto UTP-Ridda2
Utilizando Open Journal Systems