Forecasting Next-Day Electricity Prices by Time Series Models
http://www.erasmusenergy.com/articles/61/1/Forecasting-Next-Day-Electricity-Prices-by-Time-Series-Models/Page1.html
Francisco J Nogales
Francisco J. Nogales received the B.S. degree in mathematics from Universidad Autónoma de Madrid, Madrid, Spain, in 1995, and the Ph.D. degree in mathematics from Universidad Carlos III de Madrid in 2000. He is currently an Associate Professor of Statistics and Operations Research at the Universidad Carlos III de Madrid, Spain. His research interests include planning and economics of power systems, optimization, decomposition methods in mathematical programming problems, and forecasting. By Francisco J Nogales
Published on 10/1/2007
Keywords: Electricity markets, forecasting, market clearing
price, time series analysis.
Published in: IEEE TRANSACTIONS ON POWER SYSTEMS
Publication year: 2002
Co-author 1: Javier Contreras
Co-author 2: Antonio J. Conejo
Co-author 3: Rosario Espinola
In the framework of competitive electricity markets, power producers and consumers need accurate price forecasting tools. Price forecasts embody crucial information for producers and consumers when planning bidding strategies in order to maximize their benefits and utilities, respectively. This paper provides two highly accurate yet efficient price forecasting tools based on time series analysis: dynamic regression and transfer function models. These techniques are explained and checked against each other. Results and discussions from real-world case studies based on the electricity markets of mainland Spain and California are presented.
price, time series analysis.
Published in: IEEE TRANSACTIONS ON POWER SYSTEMS
Publication year: 2002
Co-author 1: Javier Contreras
Co-author 2: Antonio J. Conejo
Co-author 3: Rosario Espinola
In the framework of competitive electricity markets, power producers and consumers need accurate price forecasting tools. Price forecasts embody crucial information for producers and consumers when planning bidding strategies in order to maximize their benefits and utilities, respectively. This paper provides two highly accurate yet efficient price forecasting tools based on time series analysis: dynamic regression and transfer function models. These techniques are explained and checked against each other. Results and discussions from real-world case studies based on the electricity markets of mainland Spain and California are presented.
Forecasting Next-Day Electricity Prices by Time Series Models
In the framework of competitive electricity markets, power producers and consumers need accurate price forecasting tools. Price forecasts embody crucial information for producers and consumers when planning bidding strategies in order to maximize their benefits and utilities, respectively. This paper provides two highly accurate yet efficient price forecasting tools based on time series analysis: dynamic regression and transfer function models. These techniques are explained and checked against each other. Results and discussions from real-world case studies based on the electricity markets of mainland Spain and California are presented.