## 6 Limitations and Future Work  
Despite the promising results achieved through the GREENIA framework, several limitations must be acknowledged, which also pave the way for future research and improvements. One of the primary limitations of this study is the reliance on meteorological and energy production data from specific regions in Greece. The accuracy and generalizability of the GREENIA framework are contingent upon the availability and quality of these data. In regions with sparse or low-quality data, the predictions and optimization outcomes may be less reliable. Future work should explore methods to enhance data collection, such as integrating remote sensing technologies or using crowdsourced weather data, to improve model performance in data-scarce regions.  
While the GREENIA framework provides valuable insights for strategic planning, it has not yet been integrated into real-time energy management systems. The ability to adapt to real-time data and dynamically adjust energy production strategies in response to changing conditions would significantly enhance the framework's utility. Future research should explore the integration of GREENIA with real-time monitoring and control systems, potentially using internet of things (IoT) devices and edge computing to enable rapid, automated decision making.  
The optimization and strategic planning processes in GREENIA primarily focus on maximizing energy production. However, they do not fully account for social and environmental factors, such as community opposition to RES placement, land use conflicts, or the potential impact on local ecosystems. Future work should incorporate multi-objective optimization techniques that balance energy production with social and environmental sustainability. Engaging with local communities and stakeholders to ensure that renewable energy projects are both effective and socially acceptable is also a critical area for future research.  
While GREENIA's forecasting component is effective in the short term, it may be subject to increased uncertainty when extended to long-term predictions. Changes in climate patterns, economic conditions, and technological advancements could all impact the accuracy of long-term forecasts. Future research could explore ways to incorporate these uncertainties into the model, potentially using probabilistic forecasting methods or scenario analysis to provide a range of possible outcomes rather than a single deterministic forecast.  
As AI and machine learning play an increasingly prominent role in energy systems, ethical considerations and policy implications must be addressed. The deployment of AI-driven energy strategies raises questions about transparency, accountability, and fairness, particularly in how decisions impact different communities. Future work should examine the ethical dimensions of AI in energy management, ensuring that the benefits of AI are distributed equitably and that decision-making processes remain transparent and accountable.  
In conclusion, while the GREENIA framework represents a significant advancement in the strategic deployment of renewable energy sources, addressing these limitations will be essential to fully realize its potential. By enhancing data quality, improving computational efficiency, and by integrating social, environmental, and ethical considerations, future research can build upon the foundation laid by GREENIA to create even more robust, flexible, and equitable energy systems.