Title :
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
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;
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
DOI :
10.1109/ICSMC.2007.4414228
