for January, the revenue meter values are multiplied by 30/31).

4.  Monthly revenue meter data are corrected for monthly availability and curtailment (i.e., monthly gross energy data are calculated).

5.  A linear regression between monthly gross energy production and concurrent density-corrected monthly average wind speeds is performed.

6.  Long-term density-corrected monthly average wind speed is then calculated for each calendar month (i.e., average January wind speed, average February wind speed, and so forth) with a hindcast approach, using 10–20 years of the available long-term reference monthly wind resource data (reanalysis products, long-term reference measurements, etc.).

7.  Slope and intercept values from the regression relationship are then applied to the long-term density-corrected monthly average wind speed data with the long-term or so-called windiness correction. A long-term data set of monthly (January, February, etc.) estimated gross energy production is obtained.

8.  The resulting long-term monthly gross energy estimates, which are based on 30-day months, are then denormalized to the actual number of days in each calendar month (e.g., for January, the obtained value is multiplied by 31/30).

9.  Long-term estimates of availability and curtailment losses are finally applied to the denormalized long-term monthly gross energy data, leading to a long-term calculation of 