DETECTION OF DUST ON SOLAR PANELS WITH DEEP LEARNING

dc.contributor.authorSefer, Tuba
dc.contributor.authorKaya, Mahmut
dc.date.accessioned2024-12-24T19:16:14Z
dc.date.available2024-12-24T19:16:14Z
dc.date.issued2024
dc.departmentSiirt Üniversitesi
dc.description.abstractSolar energy is an environmentally friendly, clean, and sustainable alternative. The widespread use of this energy source offers excellent environmental and economic benefits. However, some factors affect the efficiency of solar panels. One of these factors is dust. When dust accumulates on the surface of solar panels, it can significantly reduce the efficiency of energy production. Therefore, detecting and quickly removing dust from solar panels is crucial. Managing this process with unmanned artificial intelligence systems, especially in large areas, will provide significant advantages in terms of time and cost. In recent years, convolutional neural networks have achieved significant success in image classification. In particular, transfer learning methods have proven their success in this field. In this study, we aim to solve a new task with limited data using pre-trained deep learning models (EfficientNetB3, ResNet50, MobileNet, VGG19, Xception, InceptionResNetV2, VGG16, ResNet101, DenseNet201, EfficientNetB7) to classify dirty and clean solar panels. These models were chosen because they each have different strengths and have performed well on various tasks. The models with the best performance among these models are combined to improve classification prediction. The proposed ensemble learning approach achieved 99.31% classification accuracy by considering the prediction results of the models with a voting approach. As a result, this approach aims to optimize the maintenance processes of solar energy systems, improve energy efficiency, and support sustainable energy use in the long term.
dc.identifier.doi10.17780/ksujes.1493906
dc.identifier.endpage1464
dc.identifier.issn1309-1751
dc.identifier.issue4
dc.identifier.startpage1451
dc.identifier.trdizinid1283747
dc.identifier.urihttps://doi.org/10.17780/ksujes.1493906
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/1283747
dc.identifier.urihttps://hdl.handle.net/20.500.12604/4305
dc.identifier.volume27
dc.indekslendigikaynakTR-Dizin
dc.language.isoen
dc.relation.ispartofKSÜ Mühendislik Bilimleri Dergisi
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241222
dc.subjectDeep learning
dc.subjectenergy efficiency
dc.subjecttransfer learning
dc.subjectensemble learning
dc.subjectsustainable energy
dc.titleDETECTION OF DUST ON SOLAR PANELS WITH DEEP LEARNING
dc.typeArticle

Dosyalar