## 5 Comparative Analysis of Renewable Energy Deployment Parameters  
In the strategic planning for renewable energy deployment, various critical aspects affect the best location of RESs, encompassing geographic, environmental, and social considerations. The GREENIA framework offers a holistic and comprehensive approach to the deployment of RESs by integrating advanced ML techniques, multi-source optimization, and explicability through SHAP analysis. To further validate the effectiveness and uniqueness of the framework, we compare its methodology and results with existing studies on renewable energy optimization. The following comparative analysis highlights the strengths of GREENIA relative to several other approaches in the field. [PERSON] et al. [27] concentrated on enhancing a hybrid renewable energy system for non-interconnected islands in Greece, with a specific emphasis on Amorgos. Their research employed linear programming to reduce the levelized cost of energy (LCOE), using wind, solar, and battery storage to achieve energy independence. This technique was used to overcome the distinct energy difficulties of isolated islands, although it predominantly centered on a singular case study with restricted applicability to other geographic contexts. In contrast, the GREENIA framework goes beyond optimizing hybrid systems by considering a wide array of renewable energy sources, such as wind, solar, geothermal, hydro, and biomass, across mainland Greece. By employing SHAP analysis for transparency and the Jaya algorithm for optimization, GREENIA provides a more comprehensive solution that not only addresses technical aspects but also incorporates social and environmental sustainability, ensuring that renewable energy deployment is well aligned with community needs and long-term ecological goals. Furthermore, [PERSON] et al. [31] concentrated on enhancing the siting of onshore wind farms, especially addressing wake impacts and topographical considerations. Their application of particle swarm optimization (PSO) was efficacious for optimizing wind farm layouts; however, it is constrained to the technological dimensions of wind energy production. Furthermore, their research focused predominantly on cost efficiency and the reduction of energy losses, neglecting multi-source energy systems and the wider social and environmental implications. Conversely, the GREENIA framework integrates many energy sources and employs a more adaptable optimization method suitable for a wider array of geographic and environmental contexts. Moreover, GREENIA considers community consultation, land availability, and environmental impact assessments, rendering it a more comprehensive solution that harmonizes technological optimization with social and environmental considerations. Considering distributed energy placement, [PERSON] et al. [32] addressed the optimal distributed generation placement (ODGP) problem using uPSO to reduce power losses in distribution networks. While this approach was highly efficient for minimizing energy losses and optimizing distributed energy resource (DER) placement, it was largely focused on technical optimization within the grid and did not take into account social, environmental, or economic factors that might influence the long-term sustainability of renewable energy projects. The GREENIA framework integrates multi-source optimization and addresses technical, social, and environmental considerations. It goes beyond simply reducing energy losses by incorporating community engagement and environmental impact assessments to ensure that the deployment of renewable energy sources is both technically optimal and socially sustainable. Finally, another main component of the GREENIA framework is the XAI. [PERSON] et al. [33] explored the use of XAI and packetized energy management (PEM) in industrial energy systems, focusing particularly on optimizing energy loads in industrial processes using networked sensing and software-defined energy networks.