A Hybrid Forecasting Method for Day-ahead Electricity Price Based on GM(1,1) and ARMA
Under deregulated environment, accurate price forecasting provides crucial information for electricity market participants to make reasonable competing strategies. With comprehensive consideration of the changing rules of the day-ahead electricity price of the United States PJM electricity market, a day-ahead electricity price forecasting method based on grey system theory and time series analysis is developed, in which the equal-dimension and new-information GM(1,1) model is firstly used to the raw data of electricity price series, and then the autoregressive moving average (ARMA) model is used to the grey residual series. The numerical example based on the historical data of the PJM market from July to September in 2007 shows that the method can reflect the characteristics of electricity price better and the forecasting accuracy can be improved virtually compared with the conventional GM(1,1) model.
Ruiqing Wang Lian Yao Yuzeng Li
School of Computer and Information Engineering, Anyang Normal University, Anyang 455002, Henan Provi Department of Electrical Information and Electrical Engineering, Anyang Institute of Technology, Any Department of Automation, Shanghai University,Shanghai 200072, China
国际会议
2009 IEEE International Conference on Grey System and Intelligent Services(2009 IEEE灰色系统与服务科学国际会议)
南京
英文
577-581
2009-10-20（万方平台首次上网日期，不代表论文的发表时间）