## 2 Literature Review
For the prediction of solar power production, [PERSON] et al., 2019 [21] suggested a novel hybrid optimization technique termed hybrid improved multi-verse optimizer (HIMVO). Their HIMVO algorithm was used to optimize the support vector machine (SVM) for photovoltaic output prediction by introducing chaotic sequences to initialize the population, which significantly improved the algorithm's convergence rate in comparison with other optimization algorithms such as the multi-universe optimizer algorithm, particle swarm optimization algorithm, and dragonfly algorithm. Their results show that the HIMVO method had higher optimization capability and stability, and that the HIMVO-SVM model had superior prediction accuracy and stability compared with other models. [PERSON] et al., 2018 [PERSON] et al. (2018) introduced a hybrid forecasting model, hybrid WT-PSO-SVM, for short-term (one day in advance) production power forecasting of a real microgrid PV system. Using wavelet transform, particle swarm optimization, and support vector machine, the model enhanced the accuracy of forecasts. Using interactions between PV system supervisory control and data acquisition (SCADA) real power records and numerical weather prediction (NWP) meteorological data, the model adjusted the SVM parameters using particle swarm optimization (PSO) to improve forecasting precision. In comparison with seven previous forecasting methodologies, the suggested model demonstrated superior predicting accuracy. Mean absolute percentage error (MAPE) is 4.2% on a daily basis, whereas normalized mean absolute error (NMAE) is 0.4%. Moreover, incorporating XAI into the GREENIA framework allows for greater transparency and understanding of how various environmental factors influence energy production predictions. The use of Shapley additive explanation (SHAP) values in GREENIA exemplifies this approach, providing insights into the contributions of different variables such as wind speed and temperature, towards the model's predictions ([PERSON] et al., 2018). This transparency is vital for building trust among stakeholders, including policymakers and investors, who require assurance that AI-driven decisions are both informed and justifiable ([PERSON] et al., 2018). The positioning of RES is essential for maximizing energy output and improving grid dependability. Diverse decision-making systems have been established to enhance this process, including geographic information systems (GISs) and optimization techniques. Geographic decision support systems (GDSSs) utilize GISs to identify optimal locations for distributed energy resources (DERs). This approach has aided utility companies in making informed decisions regarding DER portfolios, as demonstrated in Claremont, California ([PERSON] et al., 2018). More particularly, in Greece, a comprehensive sitting procedure was developed using GISs to address the challenges of RES placement. This method considered multiple parameters affecting site selection, exemplified by a case study on the island of Syros ([PERSON], 2018). While existing frameworks, such as GDSS and optimization techniques like unified particle swarm optimization (UPSO) ([PERSON] et al., 2018), have been instrumental in improving RES placement, the GREENIA framework introduces a new level of sophistication. By integrating advanced ML models, such as GRUs, with evolutionary algorithms like the Java optimization technique, GREENIA not only predicts energy output with accuracy but also optimizes RES placement based on dynamic meteorological and geographical data. Furthermore, the use of XAI techniques within GREENIA ensures that the decision-making process is transparent, providing insights into the key factors influencing energy production. This level of interpretability, combined with prediction and optimization capabilities, makes GREENIA a powerful tool for policymakers and energy planners, driving more effective, data-driven decisions for renewable energy deployment.