SolPowNet: Dust Detection on Photovoltaic Panels Using Convolutional Neural Networks

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dc.contributor.authorAlcin, Omer Faruk
dc.contributor.authorAslan, Muzaffer
dc.contributor.authorAri, Ali
dc.date.accessioned2026-04-04T13:31:08Z
dc.date.available2026-04-04T13:31:08Z
dc.date.issued2025
dc.departmentİnönü Üniversitesi
dc.description.abstractIn recent years, the widespread adoption of photovoltaic (PV) panels for electricity generation has provided significant momentum toward sustainable energy goals. However, it has been observed that the accumulation of dust and contaminants on panel surfaces markedly reduces efficiency by blocking solar radiation from reaching the surface. Consequently, dust detection has become a critical area of research into the energy efficiency of PV systems. This study proposes SolPowNet, a novel Convolutional Neural Network (CNN) model based on deep learning with a lightweight architecture that is capable of reliably distinguishing between images of clean and dusty panels. The performance of the proposed model was evaluated by testing it on a dataset containing images of 502 clean panels and 340 dusty panels and comprehensively comparing it with state-of-the-art CNN-based approaches. The experimental results demonstrate that SolPowNet achieves an accuracy of 98.82%, providing 5.88%, 3.57%, 4.7%, 18.82%, and 0.02% higher accuracy than the AlexNet, VGG16, VGG19, ResNet50, and Inception V3 models, respectively. These experimental results reveal that the proposed architecture exhibits more effective classification performance than other CNN models. In conclusion, SolPowNet, with its low computational cost and lightweight structure, enables integration into embedded and real-time applications. Thus, it offers a practical solution for optimizing maintenance planning in photovoltaic systems, managing panel cleaning intervals based on data, and minimizing energy production losses.
dc.identifier.doi10.3390/electronics14214230
dc.identifier.issn2079-9292
dc.identifier.issue21
dc.identifier.orcid0000-0002-2917-3736
dc.identifier.orcid0000-0002-5071-6790
dc.identifier.orcid0000-0002-2418-9472
dc.identifier.scopus2-s2.0-105021396201
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.3390/electronics14214230
dc.identifier.urihttps://hdl.handle.net/11616/108594
dc.identifier.volume14
dc.identifier.wosWOS:001612532000001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofElectronics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250329
dc.subjectphotovoltaic panel
dc.subjectdust detection
dc.subjectclassification
dc.subjectCNN
dc.subjectdeep learning
dc.subjectelectrical efficiency
dc.titleSolPowNet: Dust Detection on Photovoltaic Panels Using Convolutional Neural Networks
dc.typeArticle

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