Shown here is a distribution of wind speeds. This could be the actually data or it could be a Weibull distribution. In this case it’s actually the Mt Tom data. So it’s the number of hours the wind blew at different wind speeds in different bins over a month. So in this case it’s not annual data, but monthly data, and to overlay on top of this, a wind turbine power curve. You typically don’t see power curves drawn out like this, but we’re using a bar chart to show sort of where they overlap and really to signify that we’re going to multiply these two values together. So the amount of power that will be produced within the bin of each wind speeds is going to be multiplied by the number of hours that it spends at that wind speed. If we used instead the actual probability which sums up to one, then this would give you the average power rather than the an energy output for the site. So lets go ahead and show what happens when you multiply those together. You get the red chart here in the middle. And you might notice that the peak of this chart, or the wind speeds that provide the most power, and energy are in the 0.8 to 9 m/s range, where as the most prevalent wind speeds were back here in about the 5 m/s range. And of course this is a combination because of the power curve, but also because the power in the wind is a function of the cube of the velocity, so there is usually a higher wind speed that gives you the most power output than the one that is the most prevalent.