Cloud-Integrated Smart Detection of Solar Panel Damage and Degradation

There is a high importance and need for efficient and scalable systems to supervise and maintain the performance of solar panels, especially in big installation setups, considering the faster progress in using solar power among households and industries. Small-scale installations are less complicated to deal with, but for large grids of solar panels, companies face challenges in foresight and determination of issues like damage, decreased performance, and it also affects the environment. Most current solutions depend on time-consuming and errorprone human inspections or stand-alone monitoring systems. Our project proposes to overcome these limitations by introducing a cloud-based intelligent grid energy management system. The technology predicts the performance of solar panels and detects potential damage by employing simulated data, machine learning algorithms, and AWS cloud platforms, enabling predictive maintenance. With a simple, web-based dashboard it displays in the outputs, enhancing decisionmaking, reducing downtime of solar panels, and providing realtime information about panel conditions. This approach involves predictive analytics combined with data processing based on the Amazon web services tools, which makes it highly scalable and efficient in handling large grids of solar panels.