A GARCH forecasting model to predict day-ahead electricity prices

被引：475

作者：

Garcia, RC ^{[1
]}

Contreras, J

van Akkeren, M

Garcia, JBC

机构：

[1] German Inst Econ Res, Dept Energy Transportat & Environm, DIW, D-14195 Berlin, Germany

[2] Univ Castilla La Mancha, ETS Ingn Ind, E-13071 Ciudad Real, Spain

[3] PMI Grp, Walnut Creek, CA 94957 USA

[4] Dexia Bank, Derivat Grp, Brussels, Belgium

来源：

IEEE TRANSACTIONS ON POWER SYSTEMS | 2005年 / 20卷 / 02期

关键词：

electricity markets; forecasting; GARCH models; time series analysis; volatility;

D O I：

10.1109/TPWRS.2005.846044

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Price forecasting is becoming increasingly relevant to producers and consumers in the new competitive electric power markets. Both for spot markets and long-term contracts, price forecasts are necessary to develop bidding strategies or negotiation skills in order to maximize profits. This paper provides an approach to predict next-day electricity prices based on the Generalized Autoregressive Conditional Heteroskedastic (GARCH) methodology that is already being used to analyze time series data in general. A detailed explanation of GARCH models is presented and empirical results from the mainland Spain and California deregulated electricity-markets are discussed.

引用

页码：867 / 874

页数：8

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