## 4 Results
### Optimization Performance  
The optimization phase of the GREENIA framework utilized the Jaya optimization algorithm to enhance the energy production forecasts over a 30-day period. This subsection provides a comprehensive analysis of the quantitative impact of the optimization process, supported by the results shown in Figure 9. The results of the optimization phase demonstrated a significant improvement in energy production forecasts. The pre-optimization forecasted values, depicted by the blue dashed line in Figure 9, were consistently improved upon by the post-optimization values, represented by the orange dashed line. Notably, the light blue bars at the bottom of the graph illustrate the magnitude of improvement for each day. Figure 8: Daily forecasting through AR mechanism. The cumulative effect of the optimization process was significant. The total improvement in forecasted energy production over the 30-day period amounted to 145.57 MWh, highlighting the substantial impact of the optimization process on overall energy output. On average, the optimization process yielded an improvement of 61.60 MWh per day. This consistent daily enhancement underscores the effectiveness of the Jaya optimization algorithm in refining the input features to achieve better energy production forecasts. Although the magnitude of improvements varied across the 30-day period, the optimization consistently improved the forecasted values. On some days, the gains were marginal, suggesting that the initial predictions were already close to optimal. On other days, the improvements were substantial, indicating the optimization's potential to correct less accurate forecasts effectively. The success of the Jaya optimization algorithm in enhancing energy production forecasts can be attributed to several key factors, as follows:  
* Objective Function: The objective function was crucial in the optimization process, aiming to maximize the energy production forecast. By minimizing the negative forecasted value, the algorithm effectively manipulated the solution towards optimal conditions that enhanced energy output. * Algorithmic Flexibility: The Jaya algorithm's lack of dependency on specific parameters, such as population size or crossover rate, allowed it to adapt effectively to the problem at hand, focusing solely on improving the objective function. * Convergence and Stability: For 3000 iterations for each day, the algorithm demonstrated stable convergence toward better solutions, as indicated by the overall trend of improvements. The comparison between pre- and post-optimization results, summarized as follows, underscores the significant value of integrating optimization algorithms within the GREENIA framework:  
* Baseline vs. Optimized Forecasts: Before optimization, the model's forecasts were generally accurate in capturing overall trends but often underestimated energy production, particularly on days with higher variability. The optimization process significantly improved the model's ability to capture these fluctuations, resulting in forecasts that more closely aligned with actual energy production levels. Figure 9: Optimization results for the forecasted energy. * Implications for Strategic Energy Planning: The improvements achieved through optimization have direct implications for strategic planning in renewable energy deployment. By providing more accurate forecasts, the optimized model allows for better resource allocation and operational planning, enhancing the efficiency and reliability of energy production systems. Figure 9 illustrates the crucial role of optimization in the GREENIA framework. The consistent improvements observed across the 30-day period validate the effectiveness of the Jaya optimization algorithm in refining energy production forecasts, ultimately contributing to the framework's goal of optimizing renewable energy production.