# Strategies of Automated Machine Learning for Energy Sustainability in Green Artificial Intelligence
## 8 Conclusions  
This study has highlighted the importance of considering energy efficiency in the development and optimization of automated machine learning (AutoML) models. Energy efficiency metrics for advanced hyperparameter optimization algorithms within AutoML have been introduced and examined.  
In this context, the potential of algorithms such as Bayesian optimization HyperBand (BOHB), HyperBand, population-based training (PBT), and asynchronous successive halving algorithm (ASHA) to reduce the carbon footprint of AutoML has been specifically investigated. These algorithms have been applied in image classification using an EfficientNet architecture. In addition, in the case of the PBT and BOHB algorithms, they have also been implemented in a BiLSTM architecture to solve a natural language processing problem, specifically in sentiment analysis. The findings indicate a reduction of 28.7% in \(CO_{2}e\) emissions when implementing the Green AI strategy, compared to the Red AI strategy. This improvement in sustainability is achieved with a minimal decrease of 0.51% in validation accuracy. These results suggest that the strategy of using these algorithms, focused on energy efficiency metrics, can contribute to mitigating the carbon footprint of AutoML. These metrics allow for the evaluation and optimization of an algorithm's energy consumption, taking into account not only accuracy but also sustainability and reduced environmental impact.  
The experimentation has demonstrated the application of Green AI principles to AutoML hyperparameter optimization algorithms. The current sustainability of AutoML practices has been evaluated, and strategies have been proposed to make them more environmentally friendly. The findings suggest that careful consideration of energy efficiency metrics can promote environmentally friendly computational processes in AutoML.  
As future work, these findings underscore the importance of expanding research into efficient and eco-friendly algorithms, as well as strategies for AI sustainability. This approach should consider not only economic viability but also the dimensions of social responsibility and environmental management. Through a comprehensive assessment of the carbon footprint and consideration of various aspects of AI operations, we can move toward environmentally sustainable AI practices.  
Figure 5: The graph provides a visual representation of the distribution of the validation accuracy metric in the context of Green and Red artificial intelligence (AI) strategies for the IMDb dataset for the PBT and BOHB algorithms. The figure shows the median and quartiles, providing a comprehensive view of the distribution of this metric in both AI strategies.  
**Author Contributions:** All the authors have contributed equally to the realization of this work. [PERSON] and [PERSON] participated in the conception and design of the work; [PERSON] and [PERSON] reviewed the bibliography; [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] wrote and edited the paper. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was partially possible thanks to the collaboration and support of the Spanish Ministry of Science and Innovation with the projects PDC2022-134013-I00, TED2021-131019B-I00, and PID2019-107228 RB-I00.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The dataset used is in the public domain. The code can be requested from the authors.  
**Conflicts of Interest:** The authors declare no conflicts of interest.