Design of a Method Using Machine Learning Techniques for the Maintenance and Diagnosis of Failures in Solar Panels

The increasing adoption of solar energy demands efficient maintenance strategies to ensure optimal performance and longevity of solar panel installations. This paper proposes a novel method leveraging machine learning techniques for the proactive maintenance and accurate diagnosis of failures in solar panels. The objective is to enhance the reliability and efficiency of solar energy systems while minimizing downtime and operational costs. The proposed method aims to empower solar panel operators with a proactive and intelligent maintenance system, allowing them to address potential issues before they escalate. By minimizing downtime and optimizing performance, this approach contributes to the sustainability and economic viability of solar energy installations. The effectiveness of the proposed method is demonstrated through experimental results and comparisons with traditional maintenance approaches.