# Strategies of Automated Machine Learning for Energy Sustainability in Green Artificial Intelligence
## 6 Results
### Experiment Red AI with CIFAR  
In this experiment, as the first case, the performance of an EfficientNet-B0 model was evaluated during training and validation in an AutoML environment [27]. The performance metrics evaluated were \(CO_{2}e\) emissions, accuracy, validation accuracy, and energy consumption (Table 1). The optimization of the parameters was carried out with respect to accuracy. Experiments were conducted with 10 and 100 trials. Five repetitions of the same experiment were conducted, and the mean (or average) of the results was calculated. This approach helps achieve a more precise and robust view of the model's performance by mitigating the impact of random variations or errors in a single execution, thereby enabling a more reliable estimation of overall model performance. Based on the experimental results, the models that demonstrated the highest validation accuracy for each algorithm were identified. This criterion was established to ensure optimal model performance on unseen data. Subsequently, from this set of high-precision validation models, those that exhibited the lowest equivalent \(CO_{2}e\) emissions were selected. An assessment was conducted to ascertain the average \(CO_{2}e\) (g/kWh) for each algorithm, providing insightful perspectives on the environmental sustainability of the methods employed in AutoML experimentation. The aggregated metric of \(CO_{2}e\) (g/kWh) enables the comparison of the environmental performance of different algorithms, promotes informed decision-making concerning sustainability and efficiency, identifies algorithms suitable for sustainable production environments, and raises awareness regarding the carbon footprint associated with machine learning processes. In the Red AI experiments, the results delineated in Tables 1 and 2, derived from a set of 10 trials, are articulated in terms of energy efficiency (quantified in CO\({}_{2}\)e emissions per kilowatt-hour, g/kWh) and validation accuracy. These findings indicate that the PBT algorithm ostensibly exhibits the highest energy efficiency, with a mean of 0.854 g/kWh of \(CO_{2}e\) emissions, closely trailed by ASHA with a mean of 0.926 g/kWh. However, it is \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Algorithm** & **Validation Accuracy** & **\(E_{Total}\) (w)** & **Time (s)** & **Ir** & **Epoch** & **Optimizer** & **Bach** & \(CO_{2}e\) \\  & & **Accuracy** & & & & & **(g/kWh)** \\ \hline ASHA & 47.2 & 3065.44 & 506.02 & 0.0015 & 5 & AD & 16 & 0.582 \\ ASHA & 68.68 & 3300.40 & 501.50 & 0.00125 & 5 & Rprop & 16 & 0.627 \\ ASHA & 78.91 & 8729.97 & 1188.48 & 0.0015 & 20 & AD & 32 & 1.659 \\ ASHA & 80.11 & 1433.52 & 206.57 & 0.001 & 5 & Rprop & 64 & 0.272 \\ BOHB & 67.14 & 2088.49 & 299.37 & 0.001 & 5 & SGD & 32 & 0.397 \\ BOHB & 68.78 & 6096.27 & 787.88 & 0.00125 & 20 & SGD & 64 & 1.158 \\ BOHB & 79.15 & 8215.46 & 1154.96 & 0.001 & 20 & Rprop & 32 & 1.561 \\ BOHB & 81.63 & 6211.69 & 795.02 & 0.00125 & 20 & Rprop & 64 & 1.180 \\ HB & 45.45 & 4412.82 & 504.52 & 0.0015 & 5 & AD & 16 & 0.838 \\ HB & 67.02 & 18,285.95 & 1967.38 & 0.00125 & 20 & AD & 16 & 3.474 \\ HB & 79.61 & 3414.21 & 376.82 & 0.001 & 10 & Rprop & 64 & 0.649 \\ HB & 80.06 & 882.07 & 231.42 & 0.001 & 10 & Rprop & 64 & 0.168 \\ PBT & 51.63 & 3734.77 & 505.15 & 0.0015 & 5 & AD & 16 & 0.710 \\ PBT & 63.92 & 3206.16 & 474.56 & 0.00125 & 5 & SGD & 16 & 0.609 \\ PBT & 78.84 & 2020.27 & 261.97 & 0.001 & 5 & Rprop & 64 & 0.384 \\ PBT & 80.32 & 6119.05 & 782.53 & 0.001 & 20 & AD & 64 & 1.163 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The table elucidates the findings of the experiment conducted within the Red AI. Ten trials were executed, showing the selection of the two best and worst results, each comprising five iterations of the ASHA, BOHB, HB, and PBT algorithms, each with distinct hyperparameters.