Particle Swarm Optimization Based GM(1,1) Method on Short-Term Electricity Price Forecasting with Predicted Error Improvement

Under deregulated environment, accurate electricity price forecasting is a crucial issue concerned by all market participants. Experience shows that single forecasting model is very difficult to improve the forecasting accuracy due to the complicated factors affecting electricity prices. In this paper, a particle swarm optimization based GM(1,1) method on short-term electricity price forecasting with predicted error improvement is proposed, in which the moving average method is used to process the raw data, the particle swarm optimization based GM(1,1) model is used to the processed series, and the time series analysis is used to further improve the predicted errors. 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(1,1) model. The forecasted prices accurate enough to be used by electricity market participants to prepare their bidding strategies.