Machine Learning-Driven Analysis and Prediction of Solar Power Generation for Enhanced Energy Management

This research explores and investigates the use of Machine Learning (ML) to study, analyse, predict and visualize solar power generation. Using real time data from solar power plants, which includes important key metrics like AC Power, DC Power, Daily Yield, Total Yield, combined with weather data and their corresponding time stamps. Irradiance values are also included to deduce the proportionality factor between sunlight and energy production. By employing advanced ML techniques, this research aims to increase the predictive accuracy in solar power generation, optimize energy management, identify trends and patters in solar power generation for effective decision making. The outcome of this research significantly contributes to the operational efficiency and reliability of Solar Power generation, thereby promoting a cleaner and more sustainable future.