## 7 Conclusions  
The development and deployment of RESs are pivotal in the global transition towards a sustainable future. In this context, the GREENIA framework emerges as a pioneeringsolution tailored specifically to optimize the strategic placement and utilization of RESs in Greece. This framework not only integrates advanced machine learning techniques with powerful optimization algorithms but also emphasizes transparency and interpretability, which are crucial for informed decision-making in energy management. The current study highlights the significant advancements achieved through the GREENIA framework. By employing predictive models, such as GRU with attention mechanisms, and coupling them with robust optimization strategies like the Java algorithm, GREENIA delivers marked improvements in forecasting accuracy and optimized energy outputs. These improvements are quantitatively demonstrated through the substantial gains in energy production forecasts, as well as the strategic insights provided for deploying RESs across Greece. The framework's integration of SHAP analysis further enhances its utility by offering a transparent view of how different meteorological factors influence energy production predictions. This transparency is essential for stakeholders who require a deep understanding of the factors driving energy outputs, ensuring that the decisions made are both data-driven and comprehensible. Moreover, the strategic planning component of GREENIA goes beyond mere optimization by offering a comprehensive roadmap for RES deployment. It considers critical meteorological variables across diverse geographical regions, ensuring that the placement of energy infrastructures like wind farms and solar panels is done in a manner that maximizes efficiency and sustainability. This level of strategic insight is invaluable for regional planners and policymakers tasked with implementing Greece's renewable energy goals. Looking forward, the GREENIA framework's potential extends far beyond this study's immediate results. As global challenges like climate change demand increasingly sophisticated solutions, frameworks like GREENIA could play a crucial role in shaping the future of energy systems. Integrating technical innovation with strategic foresight and transparency positions GREENIA as not just a tool for optimization but as a comprehensive framework for sustainable energy management. Future research and development should focus on enhancing the framework by addressing its current limitations and exploring its applicability in other regions and contexts. This includes improving data quality and availability, incorporating real-time system integration, and expanding the framework's scope to include social, environmental, and ethical considerations. As the framework evolves, it could be adapted to accommodate the complexities of different energy markets, regulatory environments, and technological advancements, further solidifying its role in the global energy transition. In conclusion, this research is not only able to advance the scientific understanding of renewable energy system optimization through advanced deep learning and evolutionary algorithms but also provides significant social benefits. The GREENIA framework contributes to a more sustainable energy future by optimizing the strategic placement of RESs, leading to enhanced efficiency and energy output. This optimization process can directly support policy making, improve energy independence, and reduce carbon emissions, aligning with the broader societal goals of combating climate change and ensuring a reliable and equitable energy supply. By providing explainable, data-driven recommendations, GREENIA empowers stakeholders, including policymakers, energy planners, and local communities, ensuring that energy solutions are transparent and aligned with community needs. Conceptualization, K.S. and T.K. ; Methodology, K.S. and T.K. ; Software, K.S. ; Validation, K.S. ; Data curation, K.S. ; Writing--original draft, K.S. ; Writing--review & editing, T.K.