Optimizing Solar Power Generation: Predictive Maintenance and Fault Detection Using Machine Learning Meta Classifiers

Harnessing solar energy for power generation is a burgeoning trend, with innovations abound in deploying various solar-powered equipment. This approach is not only benign but also significantly mitigates pollution, offering an eco-friendly solution. Governments are incentivizing the adoption of solar power harvesting methods through concessions and support. Solar power generation entails two crucial subsystems akin to sensor management systems. Efficient management of these subsystems involves predictive maintenance, ensuring optimal power generation by anticipating output fluctuations and scheduling panel cleaning. Identifying and replacing faulty equipment is imperative for ensuring robust power generation in solar systems. In our proposed study, we aim to forecast the Influence of ambient and module temperatures on solar power radiation utilizing the Weka machine learning tool. Leveraging algorithms such as Gaussian Processes, SMOreg, Linear Regression, Meta classifiers like Bagging, Random Committee, and Random subspace. The built prediction models the MSE is in the range of 0.1215 to 0.315 for Gaussian processes and SMOreg classifiers. The RMSE is in the range of 0.0605 to 0.3009 for SMOreg and Bagging classifiers for the ambient, module temperatures, respectively. This predictive model holds promise for enhancing grid maintenance, optimizing accessory utilization, and identifying underperforming units for servicing, ultimately bolstering daily yield and curtailing operational costs in solar power plants.