## 4 Results
### Generation Model Validation  
To validate our generation models, we measured performance using the RMSE between the reported and predicted 30-day rolling average capacity factors in the validation set. Table 5 summarizes the results of our two generation models for both cross-validation (training plants in the US, Europe (ENTSO-E), and Australia) and external validation (non-training plants in the US, Europe (ENTSO-E), Australia, Turkiye, and India). While we included imagery from as early as 2015 for the sounding model, we focused on 2018-2022 for the generation model. This is because PlanetScope has limited imagery before 2018, and this higher-temporal-resolution is crucial for predicting the 30-day rolling capacity factor. The baseline used is again the average value of the target in the relevant training set, i.e., the average capacity factor: 0.44 for NDT plants and 0.47 for FGD plants. Compared to the sounding-level regression tasks described previously, the NDT ensemble model lead to a lower baseline RMSE of 0.27. This is due to the smoothing effect from 30 to day averaging. Our generation ensemble models combine multiple sounding model types, satellite imagery sources, and timescales to outperform this baseline significantly (alpha = 0.1) with an RMSE of 0.149 to 0.199 in almost all cases for both cross-validation and external validation. The one exception is FGD external validation, which was based on only four plants (due to limited reported data) that produced an RMSE of 0.323. These four plants include the same three in Turkiye used for sounding validation and one additional plant in India.  
Limited conclusions can be drawn from the external validation on FGD plants, as there are only four plants with reported data in this group. Sounding-level FGD results for Turkiye showed both a high RMSE and a large negative MBE (Table A3). These errors are once again reflected in the results for our generation model (Table 5, bottom row).  
Our generation models performed better on NDT than FGD plants, which is explained by both the better performance of the NDT sounding-level model as well as the reduced number of sounding-level predictions going into the FGD models due to the weather filters described in Section 3.4 that lead to periods with very little sounding-level information for some plants. These results also demonstrate that, by aggregating to a coarser temporal res  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Plant** & **Validation** & **Plant** & **RMSE** & & **MBE** & \\
**Type** & **Type** & **Count** & **ML [CI]** & **Baseline** & **ML [CI]** & **Baseline** \\ \hline NDT & cross & 73 & 0.149 [0.129, 0.162] & 0.272 & \(-\)0.014 [\(-\)0.028, 0.002] & 0.000 \\ NDT & external & 104 & 0.199 [0.188, 0.210] & 0.273 & \(-\)0.075 [\(-\)0.091, \(-\)0.058] & 0.048 \\ FGD & cross & 97 & 0.196 [0.187, 0.205] & 0.270 & 0.012 [\(-\)0.007, 0.031] & 0.000 \\ FGD & external & 4 & 0.323 [0.216, 0.384] & 0.359 & \(-\)0.252 [\(-\)0.330, \(-\)0.127] & \(-\)0.272 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Generation ensemble model performance for each plant type, during the period of 2018–2022, for training plants using cross-validation and on additional plants as external validation. Baseline results and a 90% confidence interval are also included.  
olution (from hourly generation at the sounding level to rolling 30-day average generation), our error rate reduces substantially.