NDLI: GM(1,1) forecasting method for day-ahead electricity price based on moving average and particle swarm optimization

Content Provider	IEEE Xplore Digital Library
Author	Ruiqing Wang
Copyright Year	2010
Description	Author affiliation: School of Computer and Information Engineering, Anyang Normal University, 455000, China (Ruiqing Wang)
Abstract	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 PJM electricity market, a day-ahead electricity price forecasting method based on particle swarm optimization (PSO) and GM(1,1) model is proposed, in which the moving average method is used to process the raw data of electricity price series, and then the equal-dimension and new-information GM(1,1) model is used to the processed series and the PSO is used to minimize the weighted mean absolute percent error to further optimize the grey background value. 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. The forecasted prices accurate enough to be used by electricity market participants to prepare their bidding strategies.
Starting Page	5195
Ending Page	5198
File Size	316027
Page Count	4
File Format	PDF
ISBN	9781424477371
e-ISBN	9781424477395
DOI	10.1109/MACE.2010.5535890
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-06-26
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	GM(1,1) model Optimization methods Electricity market Predictive models Economic forecasting Electricity supply industry Electricity supply industry deregulation Particle swarm optimization Electricity price forecast
Content Type	Text
Resource Type	Article

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