TY - GEN
T1 - Short-term wind power prediction using least-square support vector machines
AU - Mathaba, Tebello
AU - Xia, Xiaohua
AU - Zhang, Jiangfeng
PY - 2012
Y1 - 2012
N2 - This paper presents a short-term prediction scheme of wind power from wind speed data using Least-Square Support Vector Machines (LS-SVM). The paper develops different LS-SVM models that make use of atmospheric temperature and take advantage of the periodicity of the wind speed data. Results show that atmospheric temperature and using the periodic trend improves the predictions accuracy over the persistence model. The proposed models predict wind power within an error margin of 20% of rated power, 85% of the time.
AB - This paper presents a short-term prediction scheme of wind power from wind speed data using Least-Square Support Vector Machines (LS-SVM). The paper develops different LS-SVM models that make use of atmospheric temperature and take advantage of the periodicity of the wind speed data. Results show that atmospheric temperature and using the periodic trend improves the predictions accuracy over the persistence model. The proposed models predict wind power within an error margin of 20% of rated power, 85% of the time.
KW - Support Vector Machines
KW - Wind Power Prediction
UR - http://www.scopus.com/inward/record.url?scp=84877294437&partnerID=8YFLogxK
U2 - 10.1109/PowerAfrica.2012.6498620
DO - 10.1109/PowerAfrica.2012.6498620
M3 - Conference contribution
AN - SCOPUS:84877294437
SN - 9781467325486
T3 - IEEE Power and Energy Society Conference and Exposition in Africa: Intelligent Grid Integration of Renewable Energy Resources, PowerAfrica 2012
BT - IEEE Power and Energy Society Conference and Exposition in Africa
T2 - IEEE Power and Energy Society Conference and Exposition in Africa: Intelligent Grid Integration of Renewable Energy Resources, PowerAfrica 2012
Y2 - 9 July 2012 through 13 July 2012
ER -
