from a coalition of researchers from universities, industry, and government, supported by the Atkinson Center for a Sustainable Future at Cornell University. They include: How do wind farms with their multiple wakes interact with the atmospheric boundary layer to determine the net power that can be produced? How do uneven terrain, roughness of the land or sea surface, and turbulence above the boundary layer and turbine wakes affect unsteady loading of downstream wind turbine blades? What is the effect of the atmospheric stability (convective, neutral, or stably stratified) on the performance and loading characteristics throughout a typical daily cycle? What is the optimal placement of wind turbines in an array, so that the kinetic energy capture can be maximized and unsteady loading be minimized? The report also provides possible tools used to support this necessary research. Accuracy The correlation between wind output and prediction can be relatively high, with an average uncorrected error of 8.8% in Germany over a two-year period. See also Energy forecasting Global Energy Forecasting Competition has a track for wind power forecasting Notes References E.ON Netz. Wind Report 2004, Wind Report 2005 R. Doherty and M. O’Malley. A new approach to quantify reserve demand in systems with significant installed wind capacity. IEEE Transactions on Power Systems 20(2), pp. 587–595, 2005 Giebel G., Brownsword R., Kariniotakis G., Denhard M., Draxl C. The State-Of-The-Art in Short-Term Prediction of Wind Power A Literature Overview, 2nd Edition. Project report for the Anemos.plus and SafeWind projects. 110 pp. Risø, Roskilde, Denmark, 2011 M. Lange and U. Focken. Physical approach to short-term wind power forecast, Springer, , 2005 L. Landberg, G. Giebel, H.Aa. Nielsen, T.S. Nielsen, H. Madsen. Short-term prediction – An overview, Wind Energy 6(3), pp. 273–280, 2003 H. Madsen, P. Pinson, H.Aa. Nielsen, T.S. Nielsen and G. Kariniotakis. Standardizing the performance evaluation of short-term wind power prediction models, Wind Engineering 29(6), pp. 475–489, 2005 P. Pinson, C. Chevallier and G. Kariniotakis. Trading wind generation with short-term probabilistic forecasts of wind power, IEEE Transactions on Power Systems 22(3), pp. 1148–1156, 2007 P. Pinson, S. Lozano, I. Marti, G. Kariniotakis and G. Giebel. ViLab: a Virtual Laboratory for collaborative research on wind power forecasting, Wind Engineering 31(2), pp. 117–121, 2007 P. Pinson, H.Aa. Nielsen, J.K. Møller, H. Madsen and G. Kariniotakis. Nonparametric probabilistic forecasts of wind power: required properties and evaluation, Wind Energy, in press, 2007 External links Documentation of HiRLAM at