Int. J. of Industrial and Systems Engineering   »   2011 Vol.9, No.3

 

 

Title: Improved one day-ahead price forecasting using combined time series and artificial neural network models for the electricity market

 

Authors: Ali Azadeh; Seyed Farid Ghaderi; Behnaz Pourvalikhan Nokhandan; Shima Nassiri

 

Addresses:
Department of Industrial Engineering, Center of Excellence for Intelligent-Based Experimental Mechanics, College of Engineering, P.O. Box 11155-4563, University of Tehran, Tehran, Iran
Department of Industrial Engineering, Center of Excellence for Intelligent-Based Experimental Mechanics, College of Engineering, P.O. Box 11155-4563, University of Tehran, Tehran, Iran
Department of Industrial Engineering, Center of Excellence for Intelligent-Based Experimental Mechanics, College of Engineering, P.O. Box 11155-4563, University of Tehran, Tehran, Iran
Department of Industrial Engineering, University of Minnesota, Minneapolis, MN 55455, USA

 

Abstract: The price forecasts embody crucial information for generators when planning bidding strategies to maximise profits. Therefore, generation companies need accurate price forecasting tools. Comparison of neural network and auto regressive integrated moving average (ARIMA) models to forecast commodity prices in previous researches showed that the artificial neural network (ANN) forecasts were considerably more accurate than traditional ARIMA models. This paper provides an accurate and efficient tool for short-term price forecasting based on the combination of ANN and ARIMA. Firstly, input variables for ANN are determined by time series analysis. This model relates the current prices to the values of past prices. Secondly, ANN is used for one day-ahead price forecasting. A three-layered feed-forward neural network algorithm is used for forecasting next-day electricity prices. The ANN model is then trained and tested using data from electricity market of Iran. According to previous studies, in the case of neural networks and ARIMA models, historical demand data do not significantly improve predictions. The results show that the combined ANN–ARIMA forecasts prices with high accuracy for short-term periods. Also, it is shown that policy-making strategies would be enhanced due to increased precision and reliability.

 

Keywords: short-term forecasting; price forecasting; pricing; forecasts; competitive markets; ANN; artificial neural networks; one day-ahead forecasting; combined time series; electricity markets; bidding strategies; profit maximisation; electricity generators; ARIMA; autoregressive integrated moving average; commodity prices; accuracy; commodities; input variables; time series analysis; current prices; price values; past prices; three-layered algorithms; feed-forward algorithms; electricity prices; Iran; historical demand; predictions; historical data; short-term periods; policy-making strategies; precision; reliability; industrial engineering; systems engineering.

 

DOI: 10.1504/IJISE.2011.043138

 

Int. J. of Industrial and Systems Engineering, 2011 Vol.9, No.3, pp.249 - 267

 

Available online: 17 Oct 2011

 

 

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