Data-Driven Solar Panel Fault and Power Loss Prediction Using Image Analysis and Environmental Factors

The ever-increasing demand for renewable energy sources necessitates effective operation and maintenance (O&M) strategies for solar panel installations. This study proposes novel data-driven methodologies for solar panel fault detection and power loss prediction, aiming to optimize solar panel health and performance. The methods leverage image analysis in conjunction with environmental factors (time, month, irradiance level) for a holistic approach. A publicly available dataset of solar panel images with corresponding power loss values is utilized. Preprocessing involves feature extraction using a pre-trained Efficient Net model and dimensionality reduction with Principal Component Analysis (PCA). The exploration compares different machine learning approaches. Model performance is evaluated on the pre-processed dataset, focusing on accuracy in predicting power loss within critical regions (very low and very high). The selection of PCA components and the choice between methods are guided by system constraints and the time-accuracy trade-off. This research presents data-driven solutions for solar panel fault detection and power loss prediction, enabling prioritized maintenance and contributing to improved solar energy generation and cost optimization.