and $\sigma_{\text{tot,corr}}$ will give insights into the error arising from ignoring the correlations existing between the various uncertainty components.

Results
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Operational-Based AEP Uncertainty Contributions
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Distributions of each uncertainty component, expressed in terms of the percent coefficient of variation of the resulting AEP distributions, across all 472 wind plants are shown in Figure \[unc\_dist\]

![Operational-based AEP uncertainty distributions across projects for the different uncertainty components; mean values across projects are shown in the legend. Uncertainty values are quantified as the percent coefficient of variation of the long-term operational AEP distribution.[]{data-label="unc_dist"}](f05){width="8.3cm"}

Uncertainty connected to wind resource IAV is found to contribute the most (average 4.1% across all wind plants). The uncertainty in the linear regression model has the second-largest contribution (1.5%), followed by the uncertainty of the reference wind speed data (0.8%; here, of the reanalysis products), and revenue meter data (here, imposed at 0.5%). The long-term windiness correction has the smallest uncertainty component (0.4%). Therefore, the number of years used for the long-term windiness correction does not have a large impact on the overall uncertainty in operational AEP, at least for the sampled range of 10–20 years. Using as few as 10 years seems sufficient to give stability to 