Title :
An improved grey model for short-term electricity price forecasting in competitive power markets with punishment function
Author :
Lei, Mingli ; Feng, Zuren
Author_Institution :
State Key Lab. of Manuf. Syst. Eng., Xi´´an Jiaotong Univ., Xi´´an, China
Abstract :
In this paper, an improved GM (1,2) model for short-term 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.
Keywords :
load forecasting; particle swarm optimisation; power markets; Nordpool power market; competitive power markets; historical data; improved GM(1,2) model; improved grey model; particle swarm optimization algorithm; punishment function method; short-term electricity price forecasting; Consumer electronics; Economic forecasting; Particle swarm optimization; Power engineering and energy; Power markets; Power measurement; Power system modeling; Predictive models; Systems engineering and theory; Weather forecasting; GM (1,2) model; particle swarm optimization; power market; price forecasting; punishment function factor;
Conference_Titel :
Electronic Measurement & Instruments, 2009. ICEMI '09. 9th International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-3863-1
Electronic_ISBN :
978-1-4244-3864-8
DOI :
10.1109/ICEMI.2009.5274761
