Title :
Time-Sharing Based ARMA-GARCH Hourly Electricity Price Forecasting Approach
Author :
Ming Zeng ; Lianjun Shi ; Kuo Tian ; Lin Zheng
Author_Institution :
Energy & Power Economic Res. Advisory Centre, North China Electr. Power Univ., Beijing
Abstract :
In power market, accurate electricity price forecasting can help all market participants make optimal bidding or purchasing decisions and maximize their revenue. In recent years, much attention has been focused on the short-term electricity price forecasting. Based on the theory of ARMA-GARCH model, the paper divides the constant day series into working day series and holiday series. Then the models on them are established respectively. The results of PJM electric market show the effectiveness of the proposed methodology, which improves the accuracy of forecasting significantly and is suitable for forecasting day market clearing price and spot price and even other forecasting domains based on the time series models.
Keywords :
autoregressive moving average processes; power markets; pricing; purchasing; time series; PJM electric market; autoregressive moving average model; electricity price forecasting approach; market pricing; optimal bidding; power market; purchasing decision; time series model; time-sharing based ARMA-GARCH model; Artificial neural networks; Autoregressive processes; Economic forecasting; Load forecasting; Neural networks; Power generation; Power markets; Predictive models; Recurrent neural networks; Time sharing computer systems; ARMA-GARCH model; Electricity price forecasting; Error; Power markets; Time series analysis;
Conference_Titel :
Risk Management & Engineering Management, 2008. ICRMEM '08. International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-0-7695-3402-2
DOI :
10.1109/ICRMEM.2008.77