An Improved Grey Model for Short-term Electricity Price Forecasting in Competitive Power Markets with Punishment Function
In this paper, an improved GM (1,2) model for Shortterm price forecasting in competitive power markets with particle swarm optimization algorithm (PSO) and punishment function method (PFM) is proposed. Considering each historical data has different impact extent to forecasting value, thus the punishment function is constructed with adjustable factor; Furthermore, considering the influence of grey background-value, the PSO algorithm is adopted to optimize the punishment function factor and the grey background value weight parameter. Thus the improved forecasting model is founded. The historical data from the Nordpool power market is used for computing, and the numerical results demonstrate the validity of the improved GM(1,2) model.
GM (1,2) model power market price forecasting punishment function factor particle swarm optimization.
Mingli Lei Zuren Feng
The Systems Engineering Institute,the State Key Laboratory of Manufacturing Systems Engineering Xian Jiaotong University Xian,China
国际会议
2009 9th International Conference on Electronic Measurement & Instruments(第九届电子测量与仪器国际会议 ICEMI2009)
北京
英文
4227-4232
2009-08-16（万方平台首次上网日期，不代表论文的发表时间）