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.
Articles by this Author
Forecasting Next-Day Electricity Prices by Time Series Models
- By Francisco J Nogales
- Published 10/1/2007
- Forecasting
- Unrated
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.
Electricity price forecasting through transfer function models
- By Francisco J Nogales
- Published 11/15/2007
- Forecasting
- Unrated
Keywords (max 10): forecasting; electricity markets; time-series analysis
Published in: Journal of the Operational Research Society
Production / Publication year: 2006
Co-author 1: Conejo, Antonio J
Forecasting electricity prices in nowadays competitive electricity markets is a must for both producers and consumers because both need price estimates to develop their respective market bidding strategies. This paper proposes different transfer function models to predict electricity prices based on both past electricity prices and demands, and discuss the rationale to build them. The importance of electricity demand information is assessed. Appropriate metrics to appraise prediction quality are identified and used. Realistic and extensive simulations based on data from the PJM Interconnection for year 2003 are conducted. The models proposed are compared with naïve and other techniques.
Published in: Journal of the Operational Research Society
Production / Publication year: 2006
Co-author 1: Conejo, Antonio J
Forecasting electricity prices in nowadays competitive electricity markets is a must for both producers and consumers because both need price estimates to develop their respective market bidding strategies. This paper proposes different transfer function models to predict electricity prices based on both past electricity prices and demands, and discuss the rationale to build them. The importance of electricity demand information is assessed. Appropriate metrics to appraise prediction quality are identified and used. Realistic and extensive simulations based on data from the PJM Interconnection for year 2003 are conducted. The models proposed are compared with naïve and other techniques.