[其它]Application of GARCH Model in the Forecasting of Day-Ahead Electricity Prices

In the new deregulated electric power industry, o%|1D'f^  
price forecasting is becoming increasingly important _WSJg1  
for the producers and consumers to estimate and )pvZM?  
maximize their profits. A generalized autoregressive 2.NzB7c*CM  
conditional heteroskedastic (GARCH) methodology is a v`eA`)S  
presented to predict day-ahead electricity prices. For 4,aBNuxWd  
the high volatility of the electricity prices, the GARCH G!Uq
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model is more suitable for illustrating the time series Dnp^yqz*  
data than other forecast model adopted generally. The ;wN.RPE_^  
prediction error is assumed to be serially correlated zO+nEsf^O  
other than independent variable with zero mean and :WxMv~e{U  
constant variance, which can be modeled by an Auto M.128J+xfS  
Regressive process. Based on the initial values of the ]c5Shj5|p  
parameters of the model gained by Eviews software,
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Genetic arithmetic is used to optimize them to improve @ J"1!`  
its performance. A detailed explanation of GARCH 6-JnT_  
models is pre .. 4s"HO/  
iA< EJ  

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