much wider sampling.

-   Long-term (windiness) correction uncertainty. We incorporate this component by sampling the number of years (randomly picked between 10 and 20) to use as the long-term wind resource data to which the regression coefficients are applied to derive long-term energy production data (the so-called windiness correction).

-   Wind resource interannual variability (IAV) uncertainty. We incorporate this uncertainty component in the Monte Carlo method by sampling the long-term (reanalysis) average calendar monthly wind speeds (i.e., average January, average February) used to calculate long-term monthly energy production data. The sampling distribution is normal, centered on the calculated long-term average calendar monthly wind speed, and with a standard deviation equal to the 20-year standard deviation of the long-term average monthly wind speed for each calendar month.

Each of the listed sources of uncertainty corresponds to a Monte Carlo sampling and is highlighted by a probability distribution in the flowchart in Figure \[MC\_process\]. Note that uncertainty components related to availability and curtailment losses are not considered in our approach because the EIA 923 database does not include measurements of these losses. To calculate these uncertainty components at each wind plant, we run the Monte Carlo simulation under five different setups, each 