using a linear, quadratic, and cubic function, and calculate the mean absolute error (MAE) of each fit.

![image](f02){width="12cm"}

We find that the difference between the normalized MAE values from the considered functions is less than 0.7%. Therefore, the uncertainty connected with the choice of using a linear regression in the operational AEP methodology at monthly time resolution appears minimal. Moreover, through conversations with wind industry professionals, we found that a linear regression based on monthly data is the standard industry approach when performing bankable[^1] operational AEP analyses.

Operational AEP Methodology
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Given the lack of existing guidelines for a standard approach for *operational* AEP calculations, we base our methodology on conversations with four major wind energy consultants who represent most of the operational market share in North America. These conversations overwhelmingly revealed the following characteristics for operational AEP analysis, and we follow the same approach in our analysis:

1.  Wind speed data (measured or modeled) are density-corrected at their native time resolution, using Equation \[den\_corr\].

2.  Monthly revenue meter data, monthly average availability and curtailment losses, and monthly average wind speeds from a long-term wind resource product are calculated.

3.  Monthly revenue meter data are normalized to 30-day months (e.g., 