Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

Self-adaptive radial basis function neural network for short-term electricity price forecasting

Self-adaptive radial basis function neural network for short-term electricity price forecasting

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Generation, Transmission & Distribution — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Effective and reliable electricity price forecast is essential for market participants in setting up appropriate risk management plans in an electricity market. A reliable price prediction model based on an advanced self-adaptive radial basis function (RBF) neural network is presented. The proposed RBF neural network model is trained by fuzzy c-means and differential evolution is used to auto-configure the structure of networks and obtain the model parameters. With these techniques, the number of neurons, cluster centres and radii of the hidden layer, and the output weights can be automatically calculated efficiently. Meanwhile, the moving window wavelet de-noising technique is introduced to improve the network performance as well. This learning approach is proven to be effective by applying the RBF neural network in predicting of Mackey–Glass chaos time series and forecasting of the electricity regional reference price from the Queensland electricity market of the Australian National Electricity Market.

References

    1. 1)
      • J. Bezdek , R. Ehrlich , W. Full . FCM: the fuzzy c-means clustering algorithm. Comput. Geosci. , 191 - 203
    2. 2)
      • Ronkkonen, J., Kukkonen, S., Price, K.V.: `Real-parameter optimization with differential evolution', IEEE Congress on Evolutionary Computation, IEEE CEC 2005, Proc., September 2005, Edinburgh, Scotland, United Kingdom, 1, p. 506–513.
    3. 3)
      • S.R. Murray , T.A. Hohansen . Multiple model approaches to modeling and control.
    4. 4)
      • M.C. Mackey , J. Glass . Oscillation and chaos in physiological control systems. Science , 4300 , 287 - 289
    5. 5)
      • (Australian) National Electricity Market Management Company Limited (NEMMCO) website, http://www.nemmco.com.au.
    6. 6)
      • R.R. Coifman , D.L. Dohono . (1995) Translation-invariant de-noising, Wavelet and statistics.
    7. 7)
      • X.F. Yan , J. Yu , F. Qian , J.W. Ding . Kinetic parameter estimation of oxidation in supercritical water based on modified differential evolution. J. East China Univ. Sci. Technol. , 1 , 94 - 97
    8. 8)
      • X. Lu , Z.Y. Dong , X. Li . Electricity market price spike forecast with data mining techniques. Int. J. Electr. Power Syst. Res. , 19 - 29
    9. 9)
      • C.S. Chen , B. Mulgrew . Gradient radial basis function networks for nonlinear and non-stationary time series prediction. IEEE Trans. Neural Netw. , 1 , 190 - 194
    10. 10)
      • C.P. Rodriguez , G.J. Anders . Energy price forecasting in the Ontario competitive power system market. IEEE Trans. Power Syst. , 1 , 366 - 374
    11. 11)
      • L. Zhang , P.B. Luh . Neural network-based market clearing price prediction and confidence interval estimation with an improved extended Kalman filter method. IEEE Trans. Power Syst. , 1 , 59 - 66
    12. 12)
      • A. Staiano , R. Tagliaferri , W. Pedrycz . Improving RBF networks performance in regression tasks by means of a supervised fuzzy clustering. Neurocomputing , 1570 - 1581
    13. 13)
      • J. Contreras , R. Espinola , F.J. Nogales , A.J. Conejo . ARIMA models to predict next-day electricity prices. IEEE Trans. Power Syst. , 3 , 1014 - 1020
    14. 14)
      • R. Xia , K. Meng , Z.L. Wang , F. Qian . Online wavelet de-noising via moving frame. Acta Autom. Sin. , 9 , 897 - 901
    15. 15)
      • R.C. Garcia , J. Contreras , M. van Akkeren , J.B.C. Garcia . A GARCH forecasting model to predict day-ahead electricity prices. IEEE Trans. Power Syst. , 2 , 867 - 874
    16. 16)
      • Hu, Z., Yu, Y., Wang, Z., Sun, W., Gan, D., Han, Z.: `Price forecasting using an integrated approach', Proc. Electric Utility Deregulation, Restructuring Power Technologies, 2004.
    17. 17)
      • Storn, R., Price, K.: `Differential evolution – a simple and efficient adaptive scheme for global optimization over continuous spaces', TR-95-012, Technical, March 1995, International Computer Science Institute, Berkeley, CA.
    18. 18)
      • Wong, O.K., Dong, Y.Z., Meng, K., Yin, X.: `Hybrid model for electricity spot prices in the Australian NEM', Proc. Inaugural Symp. Electrical Energy Evolution in China and Australia, 28–30 July 2008, Palm Cove, Queensland, Australia.
    19. 19)
      • B.R. Szkuta , L.A. Sanabria , T.S. Dillon . Electricity price short, term forecasting using artificial neural networks. IEEE Trans. Power Syst. , 3 , 851 - 857
    20. 20)
      • M.E. Tipping . Sparse Bayesian learning and the relevance vector machine. J. Mach. Learn. Res. , 211 - 244
    21. 21)
      • N. Amjady . Day-ahead price forecasting of electricity markets by a new fuzzy neural network. IEEE Trans. Power Syst. , 2 , 887 - 896
    22. 22)
      • J.J. Guo , P.B. Luh . Selecting input factors for clusters of Gaussian radial basis function networks to improve market clearing price prediction. IEEE Trans. Power Syst. , 2 , 665 - 672
    23. 23)
      • J.H. Zhao , Z.Y. Dong , X. Li , K.P. Wong . A framework for electricity price spike analysis with advanced data mining methods. IEEE Trans. Power Syst. , 1 , 376 - 385
    24. 24)
      • Z. Xu , Z.Y. Dong , W. Liu , D.H. Wang , N.K. Lee . (2005) Neural network models for electricity market forecasting, Neural networks applications in information technology and web engineering.
    25. 25)
      • D. Huang , T.W.S. Chow . A people-counting system using a hybrid RBF neural network. Neural Process. Lett. , 2 , 97 - 113
    26. 26)
      • Characterising Pool Price Volatility in the Australian Electricity Market: ‘Report produced for national electricity code administrator’, 12 September 2003.
    27. 27)
      • J. Bezdek . (1981) Pattern recognition with fuzzy objective function algorithms.
    28. 28)
      • B.L. Zhang , Z.Y. Dong . An adaptive neural-wavelet model for short term load forecasting. Int. J. Electr. Power Syst. Res. , 121 - 129
    29. 29)
      • G. Li , C.C. Liu , C. Mattson , J. Lawarree . Day-ahead electricity price forecasting in a grid environment. IEEE Trans. Power Syst. , 1 , 266 - 274
    30. 30)
      • R.O. Duda , P.E. Hart . (1973) Pattern classification and scene analysis.
    31. 31)
      • Y.S. Xue , S.Y. Fei , F.Q. Bu . Upgrading the blackout defense scheme against extreme diasters. Autom. Electr. Power Syst. , 9 , 1 - 6
    32. 32)
      • L.I. Kuncheva . Initializing of a RBF network by a genetic algorithm. Neurocomputing , 273 - 288
    33. 33)
      • G.L. Chen , X.F. Wang , Z.Q. Zhuang . (1996) Genetic algorithms and its applications.
    34. 34)
      • J. Park , I. Sandberg . Universal approximation using radial basis function networks. Neural Comput. , 2 , 246 - 257
    35. 35)
      • K.P. Wong , Z.Y. Dong , K.Y. Lee , M. El-Sharkawi . (2008) Differential evolution, an alternative approach to evolutionary algorithm, Modern heuristic optimization techniques: theory and applications to power systems.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-gtd.2008.0328
Loading

Related content

content/journals/10.1049/iet-gtd.2008.0328
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address