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Notice of Violation of IEEE Publication Principles
"A Hybrid ARIMA and Neural Network Model for Short-Term Price Forecasting in Deregulated Market"
by Phatchakorn Areekul, Tomonobu Senjyu, Hirofumi Toyama, and Atsushi Yona in IEEE Transactions on Power Systems, Vol 25, No 1, February 2010
After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles.
This paper contains large portions of original text from the paper cited below. The original text was copied without insufficient attribution (including appropriate references to the original author(s) and/or paper title) and without permission.
"Time Series Forecasting Using a Hybrid ARIMA and Neural Network Model"
by G. Peter Zhang,
in Neurocomputing, Vol 50, Elsevier, 2003, pp. 159-175
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 on how to improve price forecasting accuracy. This paper provides a hybrid methodology that combines both autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) models for predicting short-term electricity prices. This method is examined by using the data of Australian national electricity market, New South Wales, in the year 2006. Comparison of forecasting performance with the proposed ARIMA, ANN, and hybrid models are presented. Empirical results indicate that a hybrid ARIMA-ANN model can improve the price forecasting accuracy.