Forecasting electricity market price spikes based on bayesian expert with support vector machines

Authors:
Wei Wu;Jianzhong Zhou;Li Mo;Chengjun Zhu
Affiliations:
College of Hydropower and Information Engineering, Huazhong University of Science and Technology, Hubei, Wuhan, P.R. China;College of Hydropower and Information Engineering, Huazhong University of Science and Technology, Hubei, Wuhan, P.R. China;College of Hydropower and Information Engineering, Huazhong University of Science and Technology, Hubei, Wuhan, P.R. China;China Three Gorges Project Corporation, Hubei, Yichang, P.R. China
Venue:
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Year:
2006

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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.