Name
Abstract | ||
|---|---|---|
In the new deregulated electric power industry, price forecasting is becoming increasingly important for the producers and consumers to estimate and maximize their profits. A generalized autoregressive conditional heteroskedastic (GARCH) methodology is presented to predict day-ahead electricity prices. For the high volatility of the electricity prices, the GARCH model is more suitable for illustrating the time series data than other forecast model adopted generally. The prediction error is assumed to be serially correlated other than independent variable with zero mean and constant variance, which can be modeled by an Auto Regressive process. Based on the initial values of the parameters of the model gained by Eviews software, Genetic arithmetic is used to optimize them to improve its performance. A detailed explanation of GARCH models is presented and empirical results from the California deregulated electricity-markets are discussed. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/ICNC.2007.252 | ICNC |
Keywords | Field | DocType |
day-ahead electricity prices,day-ahead electricity price,empirical result,electricity price,constant variance,auto regressive process,forecast model,eviews software,electric power industry,garch model,detailed explanation,electricity market,autoregressive conditional heteroskedasticity,profitability,genetics,pricing,genetic algorithms,serial correlation,time series,electric power,time series data,auto regressive,prediction error | Econometrics,Autoregressive model,Time series,Heteroscedasticity,Electricity,Computer science,Electric power industry,Variables,Autoregressive conditional heteroskedasticity,Volatility (finance) | Conference |
Volume | ISSN | ISBN |
1 | 2157-9555 | 0-7695-2875-9 |
Citations | PageRank | References |
1 | 0.38 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chengjun Li | 1 | 28 | 6.60 |
| Ming Zhang | 2 | 89 | 18.62 |