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Combination of artificial neural network and ARIMA time series models for short term price forecasting in deregulated market

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4 Author(s)
Areekul, P. ; Dept. of Electr. & Electron. Eng., Univ. of the Ryukyus, Nishihara, Japan ; Senjyu, T. ; Toyama, H. ; Yona, A.

In the framework of competitive electricity markets, power producers and consumers need accurate price forecasting tools. Price forecasts embody crucial information for producers and consumers when planning bidding strategies in order to maximize their benefits and utilities, respectively. The choice of the forecasting model becomes the important influence factor how to improve price forecasting accuracy. In this paper provides a combination methodology that combines both ARIMA and ANN models for predicting short term electricity prices. This method is examined by using the data of Australian National Electricity Market (NEM), New South Wales regional in year 2006. Comparison of forecasting performance with the proposed ARIMA and ARIMA-ANN models are presented. Empirical results indicate that an ARIMA-ANN model can improve the price forecasting accuracy.

Published in:

Transmission & Distribution Conference & Exposition: Asia and Pacific, 2009

Date of Conference:

26-30 Oct. 2009