Maximizing the efficiency of photovoltaic (PV) systems is essential for en-suring clean energy production and reducing the environmental impact of energy generation. However, PV systems are prone to a range of faults due to environmental factors such as temperature variations, solar irradiance fluctuations, dust accumulation, and humidity. These faults can lead to re-duced energy output, equipment degradation, and overall inefficiency. This paper explores the use of predictive maintenance driven by Artificial Intelli-gence (AI) to enhance the fault detection process in PV systems. The AI sys-tem integrates both environmental data (solar irradiance, temperature, hu-midity, and wind speed) and operational parameters (voltage, current, power output) to create a comprehensive predictive model. Using machine learning algorithms, the system can identify patterns in data that precede common PV faults such as module degradation, inverter failures, and cable issues. The AI-based approach predicts faults before they lead to significant perfor-mance degradation, allowing for timely interventions such as cleaning, re-pairs, or equipment replacement.

Predictive Maintenance, AI, Sustainable Energy