Short-term wind energy forecasting using support vector regression

TY - GEN

T1 - Short-term wind energy forecasting using support vector regression

AU - Kramer, Oliver

AU - Gieseke, Fabian

N1 - Conference code: 6

PY - 2011

Y1 - 2011

N2 - Wind energy prediction has an important part to play in a smart energy grid for load balancing and capacity planning. In this paper we explore, if wind measurements based on the existing infrastructure of windmills in neighbored wind parks can be learned with a soft computing approach for wind energy prediction in the ten-minute to six-hour range. For this sake we employ Support Vector Regression (SVR) for time series forecasting, and run experimental analyses on real-world wind data from the NREL western wind resource dataset. In the experimental part of the paper we concentrate on loss function parameterization of SVR. We try to answer how far ahead a reliable wind forecast is possible, and how much information from the past is necessary.We demonstrate the capabilities of SVR-based wind energy forecast on the micro-scale level of one wind grid point, and on the larger scale of a whole wind park.

AB - Wind energy prediction has an important part to play in a smart energy grid for load balancing and capacity planning. In this paper we explore, if wind measurements based on the existing infrastructure of windmills in neighbored wind parks can be learned with a soft computing approach for wind energy prediction in the ten-minute to six-hour range. For this sake we employ Support Vector Regression (SVR) for time series forecasting, and run experimental analyses on real-world wind data from the NREL western wind resource dataset. In the experimental part of the paper we concentrate on loss function parameterization of SVR. We try to answer how far ahead a reliable wind forecast is possible, and how much information from the past is necessary.We demonstrate the capabilities of SVR-based wind energy forecast on the micro-scale level of one wind grid point, and on the larger scale of a whole wind park.

U2 - 10.1007/978-3-642-19644-7_29

DO - 10.1007/978-3-642-19644-7_29

M3 - Article in proceedings

SN - 978-3-642-19643-0

T3 - Advances in Intelligent and Soft Computing

SP - 271

EP - 280

BT - Soft Computing Models in Industrial and Environmental Applications, 6th International Conference SOCO 2011

A2 - Corchado, Emilio

A2 - Snášel , Václav

A2 - Sedano, Javier

A2 - Hassanien, Aboul Ella

A2 - Calvo, José Luis

A2 - Ślȩzak, Dominik

PB - Springer

T2 - 6th International Conference on Soft Computing Models in Industrial and Environmental Applications

Y2 - 6 April 2011 through 8 April 2011

ER -