Electricity price forecasting through transfer function models
http://www.erasmusenergy.com/articles/101/1/Electricity-price-forecasting-through-transfer-function-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 11/15/2007
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.