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

Author

Mori, Hiroyuki ; Awata, Akira

Author_Institution

Meiji Univ., Kawasaki

fYear

2007

fDate

7-10 Oct. 2007

Firstpage

3743

Lastpage

3748

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.

Keywords

data mining; formal logic; power markets; pricing; radial basis function networks; regression analysis; trees (mathematics); data mining; electricity price forecasting; if-then rules; normalized radial basis function network; regression tree; Artificial neural networks; Classification tree analysis; Data mining; Economic forecasting; Electricity supply industry deregulation; Power markets; Predictive models; Radial basis function networks; Regression tree analysis; Time series analysis; Artificial Neural Network; Data Mining; Electricity Price Forecasting; Normalized Radial Basis Function Network; Time Series Analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man and Cybernetics, 2007. ISIC. IEEE International Conference on

Conference_Location

Montreal, Que.

Print_ISBN

978-1-4244-0990-7

Electronic_ISBN

978-1-4244-0991-4

Type

conf

DOI

10.1109/ICSMC.2007.4414228

Filename

4414228