Abstract:
As nations progressively go to solar based energy as valuable option in contrast to conventional
power sources, guaranteeing the proficient activity of solar power inverters becomes primitive. This
project introduces the improvement in the traditional solar-inverter systems by introducing advance
technological features including Maximum Power Point Tracking (MPPT), power prediction based
on weather using machine learning model and inverter output parameters visualization on our
custom mobile app with enhanced UI via cloud-based data management.
The project involves a design of kit to introduce smart features into traditional invertors by
incorporating Maximum Power Point Tracking (MPPT) to enhance efficiency by optimizing the
power output from solar panels under varying environmental conditions., propelling their abilities.
Also, it integrates machine learning models that are trained on historical data of weather and power
generated by solar panels. This model is used to get the anticipated power according to weather
forecast. Thereby, improving the management and utilization of energy. To provide users with
comprehensive control and insights, a mobile application is developed, offering real-time
monitoring of energy production and forecasted power production. This app enables users to track
performance, receive alerts, and make informed decisions to optimize their energy use
