Solar Panel Degradation Prediction using Machine Learning: A Comprehensive Approach

Solar photovoltaic (PV) systems are central to the world's movement toward renewable power, but their performance declines with time owing to a combination of environmental expo- sure and usage stress. In this research, we suggest a hybrid machine learning system that incorporates multi-source data such as device logs, weather history, customer endpoints, and network endpoints in order to make precise predictions about solar panel degradation. The data, which was obtained from the London Datastore and recorded by UK Power Networks for 480 days, is processed to obtain significant features capturing electrical performance as well as environmental conditions. High-level feature extraction methods were used to obtain stress measures like temperature stress, humidity stress, solar exposure, voltage drop stress, current drop stress, and total harmonic distortion (THD) stress. Fifteen regression models were trained and compared based on mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and the coefficient of determination (R2) [1, 2]. Our top-performing hybrid ensemble, which was built by stacking an artificial neural network (ANN), XGBoost, and Random Forest, recorded an R2 value above 0.96. These findings highlight the effectiveness of combining various data sources and advanced feature engineering for proactive maintenance and enhanced operational efficiency of PV systems.