Forecasting Electricity Market Price Spikes Based on Bayesian Expert with Support Vector Machines

  • Wei Wu
  • Jianzhong Zhou
  • Li Mo
  • Chengjun Zhu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4093)


This paper present a hybrid numeric method that integrates a Bayesian statistical method for electricity price spikes classification determination and a Bayesian expert (BE) is described for data mining with experience decision analysis approach. The combination of experience knowledge and support vector machine (SVM) modeling with a Bayesian classification, which can classify the spikes and normal electricity prices, are developed. Bayesian prior distribution and posterior distribution knowledge are used to evaluate the performance of parameters in the SVM models. Electricity prices of one regional electricity market (REM) in China are used to test the proposed method, experimental results are shown.


Support Vector Machine Support Vector Machine Model Mean Absolute Percentage Error Electricity Price ARIMA Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Wu, W., Zhou, J.Z., Zhu, C.J., Yang, J.J.: A No-arbitrage Equilibrium Model for the Regional Electricity Market of China. In: Proceeding of 2005 IEEE International Conference on Industrial Technology, pp. 682–687 (2005)Google Scholar
  2. 2.
    Benini, M., Marracci, M., Pelacchi, P.: Day-ahead Market Price Volatility Analysis in Deregulated electricity markets. In: Proceedings of the IEEE Power Engineering Society Summer Meeting, pp. 1354–1359 (2002)Google Scholar
  3. 3.
    Contreras, J., Espinola, R., Nogales, F.J., Conejo, A.J.: ARIMA Models to Predict Next-day Electricity Prices. IEEE Transactions on Power Systems 18, 1014–1020 (2003)CrossRefGoogle Scholar
  4. 4.
    Wang, A.J., Ramsay, B.: A Neural Network Based Estimator for Electricity Real-time- Pricing with Particular Reference to weekend and Public Holidays. Neurocomputing 23, 47–57 (1998)CrossRefGoogle Scholar
  5. 5.
    Conejo, A.J., Plazas, M.A., Espinola, R., Molina, A.B.: Day-ahead Electricity Price Forecasting Using the Wavelet Transform and ARIMA models. IEEE Transactions on Power Systems 20, 1035–1042 (2005)CrossRefGoogle Scholar
  6. 6.
    Lu, X., Dong, Z.Y., Li, X.: Electricity Market Price Spike Forecast with Data Mining Techniques. Electric Power Systems Research 73, 19–29 (2005)CrossRefGoogle Scholar
  7. 7.
    Zhao, J.H., Dong, Z.Y., Li, X., Wong, K.P.: General Method for Electricity Market Price Spike Analysis. IEEE Power Engineering Society General Meeting 1, 1286–1293 (2005)Google Scholar
  8. 8.
    Deng, N.Y., Tian, Y.J.: Support Vecter mearch: a New Approach in Data Mining. Beijing Science Press (2004)Google Scholar
  9. 9.
    Van Gestel, T., Suykens, J.A.K., Baestaens, D.-E., Lambrechts, A., Lanckriet, G., Vandaele, B., De Moor, B., Vandewalle, J.: Financial Time Series Prediction Using Least Squares Support Vector Machines within the Evidence Framework. IEEE Transactions on Neural Networks 12, 809–821 (2001)CrossRefGoogle Scholar
  10. 10.
    Cao, L.J.: Support Vector Machines Experts for Time Series Forecasting. Neurocomputing 51, 321–339 (2003)CrossRefGoogle Scholar
  11. 11.
    Mao, S.S., Wang, J.L., Pu, X.L.: Advanced Mathematical Statistics. China higher education press. China higher education press, Beijing and Springer, Berlin, Heidelberg (1998)Google Scholar
  12. 12.
    Ni, E., Luh, P.B.: Forecasting Power Market Clearing Price and Its Discrete PDF Using a Bayesian-based Classification Method. In: Proceedings of the IEEE PES Winter Meeting, pp. 1518–1523 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Wei Wu
    • 1
  • Jianzhong Zhou
    • 1
  • Li Mo
    • 1
  • Chengjun Zhu
    • 2
  1. 1.College of Hydropower and Information EngineeringHuazhong University of Science and TechnologyHubeiP.R. China
  2. 2.China Three Gorges Project CorporationHubeiP.R. China

Personalised recommendations