Abstract
This manuscript proposes a data-driven machine learning algorithm to track maximum power for PV (photovoltaic) panel systems. Data from the PV panel system connected to a boost converter has been collected. PV Voltage, current, temperature, irradiance, PI and power value have been collected for the supervised machine learning-based modeling. Where PV Voltage, PV current, temperature, and irradiance are the predictors, and PI (proportional integral) is the response of the machine learning-based model. The proposed system becomes more efficient with time while existing MPPT (maximum power point tracking) work on a specific logic for whole life. The model efficacy has been analyzed based on accuracy, scattering plot, and ROC (receiver operating characteristics) curve.
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