Data mining of electricity price forecasting with regression tree and normalized radial basis function network

Hiroyuki Mori, Akira Awata

Research output: Chapter in Book/Report/Conference proceedingConference contribution

14 Citations (Scopus)

Abstract

This paper proposes a new method for electricity price forecasting. The proposed method is based on the regression tree and NRBFN (Normalized Radial Basis Function Network) of ANN. The former is used to evaluate if-then rules and classify input data into some clusters. The latter is employed to calculate more accurate predicted values. The regression tree is one of data-mining techniques that extract if-then rules from database. NRBFN is an extension of RBFN (Radial Basis Function Network) that improves the generalization ability of RBFN. The effectiveness of the proposed method is demonstrated for real data of on-step ahead electricity price forecasting.

Original languageEnglish
Title of host publication2007 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2007
Pages3743-3748
Number of pages6
DOIs
Publication statusPublished - 1 Dec 2007
Event2007 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2007 - Montreal, QC, Canada
Duration: 7 Oct 200710 Oct 2007

Publication series

NameConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
ISSN (Print)1062-922X

Conference

Conference2007 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2007
CountryCanada
CityMontreal, QC
Period7/10/0710/10/07

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Keywords

  • Artificial neural network
  • Data mining
  • Electricity price forecasting
  • Normalized radial basis function network
  • Time series analysis

Cite this

Mori, H., & Awata, A. (2007). Data mining of electricity price forecasting with regression tree and normalized radial basis function network. In 2007 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2007 (pp. 3743-3748). [4414228] (Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics). https://doi.org/10.1109/ICSMC.2007.4414228