The global shift towards renewable energy sources, particularly solar energy, is driven by the growing need for sustainable and clean power generation. Solar energy, being abundant and environmentally friendly, plays a pivotal role in reducing carbon footprints and combating climate change. However, despite its potential, solar energy generation is inherently variable, depending on weather conditions like sunlight intensity, temperature, cloud cover, and wind speed. This variability poses significant challenges to its integration into the energy grid, as power fluctuations can lead to inefficiencies in energy distribution, especially when demand is unpredictable.

Accurate forecasting of solar energy generation is crucial for ensuring grid stability, reducing energy wastage, and optimizing the use of solar power. Traditional forecasting methods, including statistical models like linear regression, often fail to capture the complex, non-linear relationships between weather variables and energy production. As a result, the predictions provided by these systems can be imprecise, which hampers efficient grid management and the reliable utilization of solar power.