The long-term wind resource at a potential windfarm site may be estimated by correlating short-term on-site wind measurements with data from a regional meteorological station. A correlation method developed at Airtricity is described in sufficient detail to be reproduced. An assessment of its performance is also described; the results may serve as a guide to expected accuracy when using the method as part of an annual electricity production estimate for a proposed windfarm.
Wind Atlas Analysis and Application Program (WAsP), Riso National Laboratory, Denmark, 2004.
2.
WindFarmer, The Windfarm Design and Optimisation Software, Garrad Hassan and Partners Ltd, 2004.
3.
DerrickA (1993), Investigations into the Measure-Correlate-Predict strategy for site wind assessment, ETSU W/11/00236/REP, contractor: National Engineering Laboratory.
4.
Wind Energy – The Facts (2003), Appendix C; European Wind Energy Association.
5.
RiedelV.StrackM (2001), Robust approximation of functional relationships between meteorological data: Alternative measure-correlate-predict algorithms, German Wind Energy Institute.
6.
LangeM (2004), On the uncertainty of wind power predictions, in Proc “The Science of Making Torque from Wind” conference (Delft, Netherlands), Institute of Physics, University of Oldenburg, Germany.
7.
WoodsJ.C.WatsonS.J. (1997), A new matrix method of predicting long-term wind roses with MCP, inJournal of Wind Engineering and Industrial Aerodynamics66 (1997) pp 85–94.
8.
ChadeeJ.C.SharmaC (2001), Wind speed distributions: A new catalogue of defined models, in Wind Engineering, vol. 25, No. 6, pp 319–337, Dept of Electrical and Computer Engineering, University of the West Indies.
9.
MartiI. (2004), Wind power prediction in complex terrain: From the synoptic scale to the local scale, in Proc “The Science of Making Torque from Wind” conference (Delft, Netherlands), Renewable Energy National Centre of Spain.
10.
TroenI.PetersenE.L. (1989), European Wind Atlas, Riso National Laboratory, Denmark.
