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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)

Abstract

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.

Keywords

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.

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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

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