TY - GEN
T1 - Prediction of short term power output of wind farms based on least squares method
AU - Dutta, S.
AU - Overbye, T. J.
PY - 2010
Y1 - 2010
N2 - Ability to reasonably predict the power outputs of wind farms enables a better management of reserves and control mechanisms of turbines. Wind power forecasting is also important in assessing the wind energy potential of a region at the planning stages. This paper focuses on the prediction of power generation from wind farms in the short term. It proposes a least squares based method of predicting the total power output of a group of wind farms distributed over a region. A test study has been conducted by predicting the wind power output of four hypothetical wind farms located at four counties in the state of Illinois, the Bureau County, Henry County, Mercer County and Knox County. The wind speeds at the latter three counties predicted from the measured wind speeds at the former are compared to the actual measured wind speeds. Results validate the effectiveness and accuracy of the proposed method for predicting the power output of wind farms in the short term. Comparison with the Persistence Model shows that the proposed model yields superior short term wind speed predictions.
AB - Ability to reasonably predict the power outputs of wind farms enables a better management of reserves and control mechanisms of turbines. Wind power forecasting is also important in assessing the wind energy potential of a region at the planning stages. This paper focuses on the prediction of power generation from wind farms in the short term. It proposes a least squares based method of predicting the total power output of a group of wind farms distributed over a region. A test study has been conducted by predicting the wind power output of four hypothetical wind farms located at four counties in the state of Illinois, the Bureau County, Henry County, Mercer County and Knox County. The wind speeds at the latter three counties predicted from the measured wind speeds at the former are compared to the actual measured wind speeds. Results validate the effectiveness and accuracy of the proposed method for predicting the power output of wind farms in the short term. Comparison with the Persistence Model shows that the proposed model yields superior short term wind speed predictions.
KW - Cross correlation
KW - Prediction of power output
KW - Varying time lag cross correlation
KW - Wind power forecasting
UR - http://www.scopus.com/inward/record.url?scp=78649534453&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=78649534453&partnerID=8YFLogxK
U2 - 10.1109/PES.2010.5590176
DO - 10.1109/PES.2010.5590176
M3 - Conference contribution
AN - SCOPUS:78649534453
SN - 9781424483570
T3 - IEEE PES General Meeting, PES 2010
BT - IEEE PES General Meeting, PES 2010
T2 - IEEE PES General Meeting, PES 2010
Y2 - 25 July 2010 through 29 July 2010
ER -