Machine Learning-Driven MPPT Techniques for Optimal Solar Energy Utilization

In this paper, we utilized machine learning (ML) algorithms to optimize Maximum Power Point Tracking (MPPT) in photovoltaic systems. Predicting the optimal voltage is important as, at that voltage, the system gains maximum energy output and efficiency. We predicted optimal voltage from irradiance and temperature datasets by applying ML algorithm. We evaluated several ML models, finding Linear Regression to perform best with an MSE (mean square error) of 0.0024 and RMSE (root mean square error) of 0.0489. The proposed solution integrates an ML (linear regression)-driven voltage predictor, a PID controller, and a DC-DC buck-boost converter in a MATLAB simulation environment. In MATLAB Simulink, we observed that the buck-boost converter produced the same voltage as the predicted value across the load.