Forecasting electricity load by a novel recurrent extreme learning machines approach

International Journal of Electrical Power and Energy Systems

Volumn 78, Issue , 2016, Pages 429-435

  Ertugrul, Ömer Faruk ^a  

^a BATMAN UNIVERSITY   (Turkey)

Author keywords

Context neuron; Electricity load forecasting; Recurrent extreme learning machine; Recurrent neural network

Indexed keywords

ARTIFICIAL INTELLIGENCE; FORECASTING; KNOWLEDGE ACQUISITION; LEARNING ALGORITHMS; LEARNING SYSTEMS; MEAN SQUARE ERROR; NEURAL NETWORKS; RECURRENT NEURAL NETWORKS;

ELECTRICITY LOAD FORECASTING; EXTREME LEARNING MACHINE; FORECASTING DYNAMICS; FORECASTING ELECTRICITY; GENERALIZED REGRESSION NEURAL NETWORKS; MACHINE LEARNING METHODS; ROOT MEAN SQUARE ERRORS; SINGLE-HIDDEN LAYER FEED-FORWARD NEURAL NETWORK;

ELECTRIC LOAD FORECASTING;

EID: 84951152553     PISSN: 01420615     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ijepes.2015.12.006     Document Type: Article

References (32)

1. A.Y. Alanis, E.N. Sanchez, A.G. Loukianov, and E.A. Hernandez Discrete-time recurrent high order neural networks for nonlinear identification J Franklin Inst 347 7 2010 1253 1265
2. H.K. Alfares, and M. Nazeeruddin Electric load forecasting: literature survey and classification of methods Int J Syst Sci 33 1 2002 23 34 10.1080/00207720110067421
3. N. Amjady Short-term bus load forecasting of power systems by a new hybrid method IEEE Trans Power Syst 22 1 2007 333 341 10.1109/TPWRS.2006.889130
4. Aung Z, Toukhy M, Williams J, Sanchez A, Herrero S. Towards accurate electricity load forecasting in smart grids. In: The fourth international conference on advances in databases, knowledge, and data applications, DBKDA; 2012. p. 51-7.
5. T.W.S. Chow, and C.T. Leung Neural network based short-term load forecasting using weather compensation IEEE Trans Power Syst 11 4 1996 1736 1742 10.1109/59.544636
6. A. Grigorievskiy, Y. Miche, A.-M. Ventelä, E. Séverin, and A. Lendasse Long-term time series prediction using OP-ELM Neural Networks 51 2014 50 56 10.1016/j.neunet.2013.12.002
7. M.T. Hagan, and S.M. Behr The time series approach to short term load forecasting IEEE Trans Power Syst PWRS-2 3 1987 785 791 10.1109/TPWRS.1987.4335210
8. H. Hahn, S. Meyer-Nieberg, and S. Pickl Electric load forecasting methods: tools for decision making Eur J Oper Res 199 3 2009 902 907 10.1016/j.ejor.2009.01.062
9. Heeswijk MV, Miche Y, Lindh-Knuutila T, Hilbers PAJ, Honkela T, Oja E, Lendasse A. Adaptive ensemble models of extreme learning machines for time series prediction. ICANN 2009, Part II, LNCS 5769; 2009. p. 305-14. http://dx.doi.org/10.1007/978-3-642-04277-5-31.
10. 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 6 3 2013 1385 1408 10.3390/en6031385
11. H.S. Hippert, C.E. Pedreira, and R.C. Souza Neural networks for short-term load forecasting: a review and evaluation IEEE Trans Power Syst 16 1 2001 44 55 10.1109/59.910780
12. S.L. Ho, M. Xie, and T.N. Goh A comparative study of neural network and Box-Jenkins ARIMA modeling in time series prediction Comput Ind Eng 42 2 2002 371 375
13. W.C. Hong Electric load forecasting by support vector model Appl Math Model 33 5 2009 2444 2454 10.1016/j.apm.2008.07.010
14. G.B. Huang, Q.Y. Zhu, and C.K. Siew Extreme learning machine: theory and applications Neurocomputing 70 1 2006 489 501 10.1016/j.neucom.2005.12.126
15. G.-B. Huang, Q.-Y. Zhu, and C.-K. Siew Extreme learning machine: a new learning scheme of feed forward neural networks Neural Networks 2 2004 985 990
16. B. Kermanshahi Recurrent neural network for forecasting next 10 years loads of nine Japanese utilities Neurocomputing 23 1998 125 133 10.1016/S0925-2312(98)00073-3
17. R. Kohavi A study of cross-validation and bootstrap for accuracy estimation and model selection IJCAI 14 2 1995 1137 1145
18. K.Y. Lee, Y.T. Cha, and J.H. Park Short-term load forecasting using an artificial neural network Trans Power Syst 7 1 1992 124 132 10.1109/59.141695
19. Lichman M. UCI machine learning repository. Irvine, CA: University of California, School of Information and Computer Science; 2013 < http://archive.ics.uci.edu/ml >.
20. C.N. Lu, H.T. Wu, and S. Vemuri Neural network based short term load forecasting IEEE Trans Power Syst 8 1 1993 336 342 10.1109/59.221223
21. M. Lukosevicius, and H. Jaeger Reservoir computing approaches to recurrent neural network training Computer Sci Rev 3 2009 127 149 10.1016/j.cosrev.2009.03.005
22. K. Metaxiotis, A. Kagiannas, D. Askounis, and J. Psarras Artificial intelligence in short term electric load forecasting: a state-of-the-art survey for the researcher Energy Convers Manage 44 9 2003 1525 1534 10.1016/S0196-8904(02)00148-6
23. I. Moghram, and S. Rahman Analysis and evaluation of five short-term load forecasting techniques IEEE Trans Power Syst 4 4 1989 1484 1491 10.1109/59.41700
24. O. Nerrand, P. Roussel-Ragot, L. Personnaz, G. Dreyfus, and S. Marcos Neural networks and nonlinear adaptive filtering: unifying concepts and new algorithms Neural Comput 5 2 1993 165 199 10.1162/neco.1993.5.2.165
25. P.-F. Pai, and W.-C. Hong Forecasting regional electricity load based on recurrent support vector machines with genetic algorithms Electric Power Syst Res 74 2005 417 425 10.1016/j.epsr.2005.01.006
26. P.F. Pai, and W.C. Hong Support vector machines with simulated annealing algorithms in electricity load forecasting Energy Convers Manage 46 17 2005 2669 2688 10.1016/j.enconman.2005.02.004
27. D.C. Park, M.A. El-Sharkawi, R.J. Marks II, L.E. Atlas, and M.J. Damborg Electric load forecasting using an artificial neural network IEEE Trans Power Syst 6 2 1991 442 449 10.1109/59.76685
28. J. Tang, C. Deng, and G.-B. Huang Extreme learning machine for multilayer perceptron IEEE Trans Neural Networks Learn Syst PP 99 2015 1 12 10.1109/TNNLS.2015.2424995
29. Q.S. Xu, and Y.Z. Liang Monte Carlo cross validation Chemometr Intell Lab Syst 56 1 2001 1 11
30. H. Yang, and J. Ni Int J Mach Tools Manuf 45 2005 455 465
31. Z. Zhao, and M. Hu Multi-level forecasting model of coal mine water inrush based on self-adaptive evolutionary extreme learning machine Appl Math Inf Sci Lett 2 3 2014 103 110 10.12785/amisl/020305
32. Q.Y. Zhu, A.K. Qin, P.N. Suganthan, and G.B. Huang Evolutionary extreme learning machine Pattern Recogn 38 10 2005 1759 1763 10.1016/j.patcog.2005.03.028