Climate models outputs, particularly the reanalysis datasets, output wind speeds as zonal and meridional averages for the modelled time period (typically 1, 3 or 6 hours). So we get a 2-dimensional vector, the U and V values, each of which is signed. Together, they give us a mean wind speed, and a mean direction.
There are some oddities there, as I understand it. If the wind within one interval were 9 m/s due East for half the time, then 9 m/s due West for the other half, then the UV values would be (0, 0). That's a very contrived case, just to illustrate the netting off for any wind that switches sign on either the U or V directions.
Some reanalysis datasets fix this part of the problem by giving mean wind speeds as well as mean UV components.
But for the estimation of wind electricity generation , what we really need are the mean power densities ($\mathrm{W/m^2}$ in the vertical plane) of wind, at turbine hub height (typically 80m or so). That way, we can multiply the swept area of the blades, by the mean power densities, and combine with a modified version of the turbine power curve, to estimate electricity generation.
Now, the relationship between wind speeds and power densities is cubic. But the average of a cubed value is not the same as the cube of the averaged value. So, taking the cube of average wind speeds is not the same as taking the average of the cube of wind speeds. And then there's the possibility of positive or negative correlations between wind speeds and atmospheric pressure, which would also mess with the averaging.
Is there an analysis of the impact of these distortions on our ability to model & predict wind power generation?