Published Paper Details:

The rapid global transition to renewable energy sources has highlighted the need for efficient and intelligent monitoring systems for solar power generation. This project presents an AI-based Solar Electrical Power Monitoring System designed to enhance the performance, reliability, and predictive capabilities of solar photovoltaic (PV) installations. The system integrates IoT-enabled sensors to collect real-time data on electrical output, irradiance, temperature, and other environmental parameters. Artificial intelligence algorithms, including machine learning and deep learning models, are employed to forecast solar energy production, detect anomalies, and optimize energy usage. By utilizing tools such as TensorFlow, Scikit-learn, and cloud platforms like AWS or Google Cloud, the system offers automated analytics, predictive maintenance alerts, and intelligent load balancing. Visualization tools like Grafana and Blynk provide intuitive dashboards for users to monitor performance remotely. This approach not only improves operational efficiency and reduces downtime but also supports smart grid integration and sustainable energy management. The proposed system demonstrates the potential of AI in transforming conventional solar power systems into smart, adaptive, and self-optimizing energy networks.