This chapter analyzes the influence of meteorological factors on solar energy generation in microgrids and the accuracy of forecasting models. It emphasizes the importance of accurate solar generation forecasting for efficient energy management, considering parameters such as solar radiation, ambient temperature, and wind speed. The study investigates the effects of factors like solar trajectory, geographical location, and atmospheric conditions on solar energy generation and efficiency. It also highlights the challenges posed by unexpected weather variations and technical limitations of PV systems. The research shows the benefits of aggregating data into daily intervals for reducing variability in forecasting models and suggests that modern machine learning techniques are essential for improving forecast accuracy. By examining the relationship between weather parameters and solar energy efficiency, the chapter aims to enhance SPP performance and optimize solar energy systems, particularly in microgrid contexts. Meta-heuristic optimization techniques for maximizing power point tracking in PV systems are also discussed. This comprehensive approach provides critical insights for optimizing control algorithms and improving the efficiency of solar energy systems.