Assessing and Predicting Degradation of Solar Panels Using Machine Learning Approach

Solar energy has become one of the most dynamically evolving segments of the global energy sector. Solar power plants are being built worldwide, their capacity is constantly increasing, and they are becoming an integral part of the energy infrastructure in many countries. With the growing number of solar panels, the need for efficient management and maintenance of their performance is on the rise. However, effective management and maximizing the efficiency of solar panels remain complex tasks that require continuous monitoring and analysis [ 1 – 3 ].