Choice of supervised machine learning based approaches to predict PV panel faults

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This analysis compares machine learning methods for fault detection in photovoltaic systems, highlighting effectiveness and robustness.

The study evaluates multiple machine learning techniques, such as support vector machine and decision tree, for PV fault detection.
Accuracy, precision, and efficiency serve as key metrics for assessing fault diagnosis methods in photovoltaic panels.
Simulated data was utilized to train the various machine learning models and analyze their diagnostic performance.
The findings reveal the importance of data-driven techniques in enhancing reliability and performance in renewable energy systems.

Hazra et al. (2025) studied this question.

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