Next day price forecasting in deregulated market by combination of Artificial Neural Network and ARIMA time series models
Phatchakorn Areekul, Tomonobu Shah Senjyu, Naomitsu Urasaki, Atsushi Yona
Abstract
Phatchakorn Areekul, Tomonobu Shah Senjyu, Naomitsu Urasaki, Atsushi Yona
Abstract
Electricity price forecasting is becoming increasingly relevant to power producers and consumers in the new competitive electric power markets, when planning bidding strategies in order to maximize their benefits and utilities, respectively. This paper proposed a method to predict hourly electricity prices for next-day electricity markets by combination methodology of ARIMA and ANN models. The proposed method is examined on the Australian National Electricity Market (NEM), New South Wales regional in year 2006. Comparison of forecasting performance with the proposed ARIMA, ANN and combination (ARIMA-ANN) models are presented. Empirical results indicate that an ARIMA-ANN model can improve the price forecasting accuracy.
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Electricity price forecasting is becoming increasingly relevant to power producers and consumers in the new competitive electric power markets, when planning bidding strategies in order to maximize their benefits and utilities, respectively. This paper proposed a method to predict hourly electricity prices for next-day electricity markets by combination methodology of ARIMA and ANN models. The proposed method is examined on the Australian National Electricity Market (NEM), New South Wales regional in year 2006. Comparison of forecasting performance with the proposed ARIMA, ANN and combination (ARIMA-ANN) models are presented. Empirical results indicate that an ARIMA-ANN model can improve the price forecasting accuracy.
Key concepts: Autoregressive integrated moving average, Bidding, Electricity price forecasting, Electricity market, Electricity, Artificial neural network, Time series, Computer science