Solar Intelligence Predictive Models for Power Generation and Radiation

The rapid adoption of solar photovoltaics (PV) in power distribution systems demands accurate models for power generation and solar radiation prediction. We Propose develops a comprehensive framework for solar intelligence by integrating advanced machine learning techniques with diverse data sources, including satellite imagery, historical weather patterns, and geographic information. Utilizing Generative Adversarial Networks (GANs), the system enhances the accuracy of PV detection, quantification, and power generation estimation. The proposed model addresses labeled data scarcity through innovative data augmentation methods and combines location detection, PV quantification, and output estimation into a unified pipeline. The predictive capabilities are further enhanced using K-nearest neighbor regression for short-term generation forecasting. Experimental results on real-world datasets validate the robustness and scalability of the proposed solution, demonstrating its potential for optimizing energy management and grid stability