2025/07/02 by Vivek Tetarwal, Sandeep Kumar, Tetarwal, Vivek +1
Computer Science · Energy · #Computer Vision and Pattern Recognition (cs.CV) #Currency Recognition and Detection #FOS: Computer and information sciences #Information Theory (cs.IT) #Photovoltaic System Optimization Techniques #Solar Radiation and Photovoltaics
paper · pdf · doi:10.48550/arxiv.2507.01778
openalex publication_date 2025/07/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The installation of solar energy systems is on the rise, and therefore, appropriate maintenance techniques are required to be used in order to maintain maximum performance levels. One of the major challenges is the automated discrimination between clean and dirty solar panels. This paper presents a novel Dual Ensemble Neural Network (DENN) to classify solar panels using image-based features. The suggested approach utilizes the advantages offered by various ensemble models by integrating them into a dual framework, aimed at improving both classification accuracy and robustness. The DENN model is evaluated in comparison to current ensemble methods, showcasing its superior performance across a range of assessment metrics. The proposed approach performs the best compared to other methods and reaches state-of-the-art accuracy on experimental results for the Deep Solar Eye dataset, effectively serving predictive maintenance purposes in solar energy systems. It reveals the potential of hybrid ensemble learning techniques to further advance the prospects of automated solar panel inspections as a scalable solution to real-world challenges.