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2005•IEEE Transactions on Power SystemsRequires access

A GARCH Forecasting Model to Predict Day-Ahead Electricity Prices

Reinaldo C. Garcia, Javier Contreras, M. vanAkkeren, João Pedro Silva Garcia

Open publisher page 711 citations

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.

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What this paper is about

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.

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OpenAlex reports 711 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available 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.

Key concepts: Electricity price forecasting, Autoregressive conditional heteroskedasticity, Bidding, Econometrics, Electricity, Economics, Autoregressive model, Heteroscedasticity

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