# Strategies of Automated Machine Learning for Energy Sustainability in Green Artificial Intelligence
## 6 Results
### Experiment Green AI with CIFAR  
In this second experiment, the performance of an EfficientNet-B0 model was evaluated during training and validation in an AutoML environment, with the optimization process employing energy efficiency metrics [27]. The performance metrics evaluated were \(CO_{2}e\) emissions, accuracy, validation accuracy, and energy consumption (Table 4). Experiments were conducted with 10 and 100 trials, similar to the previous experiment. Each experiment was repeated five times, and the mean was calculated to enhance the robustness of the experiment. The same strategy was adopted in the data analysis as in the previous experiment, with reference to the validation accuracy and the equivalent \(CO_{2}e\) emissions (\(CO_{2}e\)(g/kWh)). In the Green AI experiments, energy efficiency and validation accuracy of various algorithms were evaluated. In terms of energy efficiency, measured in \(CO_{2}e\) emissions per kilowatt-hour (g/kWh), the BOHB algorithm proved to be the most efficient with an average of 0.794 g/kWh, followed by HB with 0.811 g/kWh. However, the median of the \(CO_{2}e\) emissions for both algorithms is significantly lower than the mean, indicating the presence of outliers. In terms of validation accuracy, the PBT algorithm outperformed the others with an average of 76.788% and a median of 77.630%, indicating greater consistency in its performance. These results were obtained from a set of 10 trials (Table 5). Upon applying the Kruskal-Wallis test to analyze ten trials of \(CO_{2}e\) emissions in Green AI, the results indicated an H value of 3.183 and a \(p\)-value of 0.3643 for \(CO_{2}e\) emissions, and an H value of 0.886 and a \(p\)-value of 0.8287 for validation accuracy. Both \(p\)-values exceed the conventional threshold of 0.05, implying the acceptance of the null hypothesis. Therefore, it is concluded that there are no significant differences in the medians of \(CO_{2}e\) nor the distributions of validation accuracy among the evaluated algorithms. Figure 2: The \(CO_{2}e\) metric results for the 100 trials conducted by Red AI and Green AI are depicted for each algorithm. The visualization highlights the best results in terms of the \(CO_{2}e\) metric optimization within the Green AI strategy and Red AI. The outliers represented are combinations of hyperparameters that result in solutions with high energy consumption and, consequently, with a high value of carbon dioxide equivalent. Outliers resulting from the experimentation are also presented. In the context of Green AI, according to the data presented in Table 6, 100 trials were conducted to evaluate the energy efficiency of various algorithms. The BOHB algorithm proved to be the most efficient, with an average \(CO_{2}e\) emission of 0.809 g/kWh, closely followed by the PBT algorithm with an average of 0.846 g/kWh. However, it is crucial to highlight that the median \(CO_{2}e\) emissions for both algorithms are lower than the mean, suggesting the presence of outliers that could be influencing the mean. With respect to validation accuracy, the PBT algorithm outperforms the others with an average of 73.918% and a median of 75.905%. This observation suggests that the PBT algorithm exhibits greater consistency in terms of accuracy performance compared to the other evaluated algorithms. The analysis of 100 trials of the \(CO_{2}e\) variable in Green AI using the Kruskal-Wallis test yielded an H = 2.177 and a \(p\) = 0.537, while for validation accuracy, an H = 2.291 and a