Site selection is a critical aspect of solar energy business strategy, and getting it right can have a significant impact on energy production and revenue. However, traditional methods of site selection, such as manual review of geographical data or rule-based systems, can be time-consuming and may not take into account all the factors that can influence optimal site selection, such as weather patterns and energy demand.

AI and machine learning (ML) can play a key role in identifying ideal solar farm sites for solar energy companies. By training an ML model on geographical data and other relevant data points, our solution can identify sites that are optimal for solar energy production, taking into account factors such as weather patterns, energy demand, and local regulations. In addition, ML models can be used to adjust solar panel placement and energy production in real-time based on weather conditions, ensuring that solar energy companies are always optimizing their energy production.