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Short-term electricity price forecasting in competitive power markets is essential both for producers and consumers in planning their operations and maximizing their benefits. This paper proposed a new grey model, called PGM(1,2), based on Particle Swarm Optimization algorithm (PSO) and correlation hours method (CHM) in order to forecast short-term price in the Nordpool market. The main sequence is...
This paper proposed a novel grey model (GM) called PSOGM(1,2,g) for short-term electricity price prediction based on Particle Swarm Optimization algorithm (PSO) and correlation method (CM) in competitive power markets. In the presented grey model, the reference sequence (RS) is defined and determined by CM. Furthermore, considering of the influence of grey background, PSO is adopted to optimize the...
In order to improve the forecasting precision of traditional grey model for short-term price in competitive electricity market, a novel grey model is presented in this paper based on period-decoupled price sequence. According to the interval time of market cleaning, the historical price data are divided into 24 sequences or 48 sequences. In the proposed grey model, two kinds of price sequences, called...
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...
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