Notice of RetractionGM(1,2) forecasting method for day-ahead electricity price based on moving average and particle swarm optimization

Notice of Retraction

After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.

We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.

The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.

Accurate electricity price forecasting provides crucial information for market players to make reasonable competing strategies under deregulated environment. With comprehensive consideration of the changing rules of the day-ahead electricity price, a day-ahead electricity price forecasting method based on particle swarm optimization (PSO) and grey GM(1,2) model is proposed, in which the moving average method is used to process the raw data series, and the grey GM(1,2) 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 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(1,2) model. The forecasted prices are accurate enough to be used by electricity market participants to prepare their bidding strategies.