## 4 Results
### Annual Validation

Table 6: Annual average capacity factor estimation performance for 2019–2022 compared to the capacity factor derived from the reported generation data for the US, Europe, (ENTSO-E), Australia, India, and Turkiye, as reported by their respective government mechanisms summarized in Appendix A.4. A comparison to the country- and fuel-specific average baseline is also included. India and Turkiye (both exclusively external validation)), the ML-based estimate had a significantly lower RMSE than the baseline approach using country averages. However, the RMSE for Europe (ENTSO-E for generation and EU ETS for emissions) external validation and all Australian plants was no better than the baseline approaches. Furthermore, for Australia, the errors were significantly worse than anywhere else with the ML-based method, underpredicting substantially. This may once again be because the ML models failed to capture the higher average utilization rate in Australia, which makes up a small percentage of training plants, relative to the lower average utilization rate of the rest of the training set. That being said, the overall plant-wise annual RMSEs of 1.75 TWh and 2.18 Mt CO2 are small relative to the annual generation and CO2 emissions of the world's largest power plants, as can be seen in Figure 7, which plots our estimates for the average annual capacity factor, total annual generation, and annual CO2 emissions at individual plants against reported electricity and emissions data. In conclusion, although our models have an RMSE of 0.12-0.24 in terms of capacity factor, they are reliable in differentiating low versus high utilization. Validating against reported data, instead of remote sensing-derived GHG estimates discussed in Section 2, provides robust and more plentiful verification of specific plants. One area for improvement is that the model struggles to predict high-capacity factors, favoring mid-range predictions instead, which contributes to a negative bias in this range. However, once the total generation and emissions are calculated from our capacity factor estimates, and compared to reported values, the bias of our models is much more evenly distributed across plants with different total generation and emissions. Looking at our external validation results, our models underestimate the reported annual emissions the most in Australia (\(n=7\)) where plants generally run at a higher capacity factor relative to the training regions according to reported generation data. On the other hand, the slight positive bias of emissions estimates in India (\(n=24\)) is more in line with the US and Europe (EU ETS), lending credibility to \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Region**} & **Validation** & **Plant** & \multicolumn{2}{c}{**RMSE (TWh)**} & \multicolumn{2}{c}{**MBE (TWh)**} \\  & **Type** & **Count** & **ML [CI]** & **Baseline** & **ML [CI]** & **Baseline** \\ \hline US & cross & 78 & 1.68 [1.51, 1.84] & 2.99 & 0.06 [\(-\)0.16, 2.90] & \(-\)0.65 \\ US & external & 6 & 2.92 [ 0.68, 3.92] & 3.90 & \(-\)0.66 [\(-\)2.17, 0.88] & 0.31 \\ Europe (ENTSO-E) & cross & 59 & 1.38 [1.09, 1.58] & 2.43 & 0.35 [ 0.15, 0.53] & \(-\)0.16 \\ Europe (ENTSO-E) & external & 27 & 1.39 [1.09, 1.61] & 1.27 & \(-\)0.55 [\(-\)0.82, \(-\)0.28] & 0.65 \\ Australia & cross & 7 & 2.05 [ 0.91, 2.60] & 2.19 & \(-\)1.46 [\(-\)2.08, \(-\)0.63] & \(-\)1.33 \\ Australia & external & 1 & 1.12 [N/A] & 0.94 & \(-\)0.49 [N/A] & 0.37 \\ India & external & 50 & 2.12 [1.78, 2.37] & 2.58 & \(-\)1.38 [\(-\)1.63, \(-\)1.12] & 0.98 \\ Turkiye & external & 8 & 2.08 [1.31, 2.57] & 2.80 & \(-\)0.94 [\(-\)1.91, 0.01] & 0.97 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Annual total generation estimation performance compared to the reported generation data (in terawatt–hours, TWh) for the US, Europe (ENTSO-E), Australia, India, and Turkiye during the period 2019–2022.