Title :
A GARCH forecasting model to predict day-ahead electricity prices
Author :
Garcia, Reinaldo C. ; Contreras, Javier ; Van Akkeren, Marco ; Garcia, João Batista C
Author_Institution :
Dept. of Energy, German Inst. of Econ. Res., Berlin, Germany
fDate :
5/1/2005 12:00:00 AM
Abstract :
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.
Keywords :
load forecasting; power markets; power system economics; time series; GARCH forecasting model; day-ahead electricity prices; deregulated electricity markets; generalized autoregressive conditional heteroskedastic methodology; price forecasting; time series data analysis; Contracts; Economic forecasting; Electricity supply industry; Electricity supply industry deregulation; Energy consumption; Job shop scheduling; Predictive models; Production; Stochastic processes; Time series analysis; Electricity markets; GARCH models; forecasting; time series analysis; volatility;
Journal_Title :
Power Systems, IEEE Transactions on
DOI :
10.1109/TPWRS.2005.846044
