## 4 Results
### Annual Validation  
Facility-level annual average capacity factor, total electricity generation, and total CO\({}_{2}\) emissions were calculated from the sounding and generation model outputs for 2019-2022, inclusive. This year range was selected because generation model training was restricted to 2018-2022 (due to the scarcity of PlanetScope data prior to 2018) and because of the overlapping nature of the 30-day windows and fiscal year differences necessitating lookbacks to the previous year. In addition to our ML models, we produced a second set of simpler baseline capacity factor estimates. We used country-level annual estimates of capacity and generation by fuel type from the EIA (eia.gov/international/data/world/electricity/more-electricity-data, accessed on 1 May 2023) and EMBER (ember-climate.org/data-catalogue/yearly-electricity-data, accessed on 1 May 2023). From this, we calculated the annual average fuel-specific capacity factor in each year reported for each country in the world. We then assumed the same capacity factors within each country for each part of the plant with the associated fuel type. This does not account for the typical variation in dispatch for plants serving base, intermediate, and peaking loads. This baseline is different to those used in Sections 4.1 and 4.2 which were simply calculated from the training data, whereas this one is based on country- and fuel-specific averages. Metrics were calculated by comparing the estimates derived from our ML methods to the reported generation data summarized in Appendix A.4 or emissions data summarized in Appendix A.6. We used this as a baseline rather than alternatives like EDGAR, ODIAC, or OCO-2/3 for the reasons described in Section 5. Table 6 displays performance metrics for the annual average capacity factor. For the US and Europe (ENTSO-E), cross-validation performance is comparable with an RMSE of 0.17 and 0.12, respectively, and external validation is 0.14 for both. The region with the highest (lowest performing) external validation RMSE was Turkiye with 0.24. The region with the largest MBE was Australia with -0.12. This may be due to Australian power plants generally running at higher capacity factors than the rest of the training set. The fifty plants in India, on the other hand, achieved results that are very similar to the US training plants, lending credibility to the generalizability of our ML emissions estimation approach. Comparing modeled to reported annual generation and emissions for individual power plants, the overall RMSEs, aggregating across both cross- and external validation, were 1.75 TWh and 2.18 Mt CO\({}_{2}\), respectively. Tables 7 and 8 show the performance of our ML methods for annual total generation (in terawatt-hour or TWh) and annual CO\({}_{2}\) emissions (in megatonnes or Mt) estimation. For US cross- and external validation, Europe (ENTSO-E for generation and EU ETS for emissions) cross-validation only, and \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Region**} & **Validation** & **Plant** & \multicolumn{2}{c}{**RMSE**} & \multicolumn{2}{c}{**MBE**} \\  & **Type** & **Count** & **ML [CI]** & **Baseline** & **ML [CI]** & **Baseline** \\ \hline US & cross & 78 & 0.17 [0.16, 0.18] & 0.22 & \(-\)0.02 [\(-\)0.05, 0.00] & \(-\)0.05 \\ US & external & 6 & 0.14 [0.12, 0.17] & 0.19 & 0.03 [\(-\)0.06, 0.12] & 0.04 \\ Europe (ENTSO-E) & cross & 59 & 0.12 [0.11, 0.13] & 0.20 & 0.04 [0.02, 0.06] & 0.05 \\ Europe (ENTSO-E) & external & 27 & 0.14 [0.12, 0.16] & 0.18 & 0.02 [\(-\)0.01, 0.05] & 0.10 \\ Australia & cross & 7 & 0.20 [0.08, 0.26] & 0.18 & \(-\)0.12 [\(-\)0.20, \(-\)0.04] & \(-\)0.10 \\ Australia & external & 1 & 0.09 [N/A] & 0.09 & \(-\)0.01 [N/A] & 0.04 \\ India & external & 50 & 0.14 [0.13, 0.15] & 0.20 & \(-\)0.03 [\(-\)0.05, \(-\)0.01] & 0.08 \\ Turkiye & external & 8 & 0.24 [ 0.21, 0.26] & 0.32 & 0.00 [\(-\)0.12, 0.10] & 0.13 \\ \hline \hline \end{tabular}
\end{table}