Diseño de redes neuronales inteligentes para la realización de pronósticos en microrredes eléctricas
DOI:
https://doi.org/10.36561/ING.17.2Palabras clave:
Redes neuronales artificiales, Pronósticos, Microrredes eléctricas, Algoritmos genéticosResumen
Conocer de antemano los perfiles de demanda y de potencia generada por las fuentes renovables constituye un aspecto esencial para la optimización de la operación de las redes eléctricas. En el caso particular de las microrredes dicho aspecto cobra aún más importancia ya que en general un alto porcentaje de la energía generada proviene de fuentes renovables. A esto debe sumársele el hecho de que debido a un efecto de escala los parámetros a pronosticar están sometidos a una gran variabilidad. En este artículo se propone una metodología para el diseño de sistemas de pronósticos basados en redes neuronales artificiales (RNA) y algoritmos genéticos.
Descargas
Citas
A. K. Singh, I. Ibraheem, S. Khatoon, M. Muazzam and D. Chaturvedi, "Load forecasting techniques and methodologies: A review," in Power, Control and Embedded Systems (ICPCES), 2012 2nd International Conference on, 2012, pp. 1-10.
A. K. Singh, S. K. Ibraheem and M. Muazzam, "An Overview of Electricity Demand Forecasting Techniques," Network and Complex Systems, vol. 3, pp. 38-48, 2013.
S. Ruzic, A. Vuckovic and N. Nikolic, "Weather sensitive method for short term load forecasting in electric power utility of Serbia," Power Systems, IEEE Transactions on, vol. 18, pp. 1581-1586, 2003.
R. F. Engle, C. Mustafa and J. Rice, "Modelling peak electricity demand," J. Forecast., vol. 11, pp. 241-251, 1992.
E. Erdem and J. Shi, "ARMA based approaches for forecasting the tuple of wind speed and direction," Appl. Energy, vol. 88, pp. 1405-1414, 2011.
R. Huang, T. Huang, R. Gadh and N. Li, "Solar generation prediction using the ARMA model in a laboratory-level micro-grid," in Smart Grid Communications (SmartGridComm), 2012 IEEE Third International Conference on, 2012, pp. 528-533.
L. Magdalena, "What is soft computing? revisiting possible answers," International Journal of Computational Intelligence Systems, vol. 3, pp. 148-159, 2010.
A. Bakirtzis, V. Petridis, S. Kiartzis, M. Alexiadis and A. Maissis, "A neural network short term load forecasting model for the Greek power system," Power Systems, IEEE Transactions on, vol. 11, pp. 858-863, 1996.
H. Chen, C. A. Canizares and A. Singh, "ANN-based short-term load forecasting in electricity markets," in Power Engineering Society Winter Meeting, 2001. IEEE, 2001, pp. 411-415.
R. L. Welch, S. M. Ruffing and G. K. Venayagamoorthy, "Comparison of feedforward and feedback neural network architectures for short term wind speed prediction," in Neural Networks, 2009. IJCNN 2009. International Joint Conference on, 2009, pp. 3335-3340.
A. Mellit and A. M. Pavan, "A 24-h forecast of solar irradiance using artificial neural network: Application for performance prediction of a grid-connected PV plant at Trieste, Italy," Solar Energy, vol. 84, pp. 807-821, 2010.
P. Lynch, "The origins of computer weather prediction and climate modeling," Journal of Computational Physics, vol. 227, pp. 3431-3444, 2008.
L. Hernandez, C. Baladrón, J. M. Aguiar, B. Carro, A. J. Sanchez-Esguevillas and J. Lloret, "Short-term load forecasting for microgrids based on artificial neural networks," Energies, vol. 6, pp. 1385-1408, 2013.
C. Chen, S. Duan, T. Cai, B. Liu and G. Hu, "Smart energy management system for optimal microgrid economic operation," Renewable Power Generation, IET, vol. 5, pp. 258-267, 2011.
(16 de noviembre, 2014). Agencia vasca de meteorología (manual de estilo). DOI: http://www.euskalmet.euskadi.net/s075853x/es/contenidos/informacion/manual_estilo/es_9900/es_nubosidad.html.
F. Heimes, G. Zalesski, W. Land Jr and M. Oshima, "Traditional and evolved dynamic neural networks for aircraft simulation," in Systems, Man, and Cybernetics, 1997. Computational Cybernetics and Simulation., 1997 IEEE International Conference on, 1997, pp. 1995-2000.
Publicado
Cómo citar
Número
Sección
