DETECTION OF DUST ON SOLAR PANELS WITH DEEP LEARNING

Tuba Sefer; Mahmut Kaya

doi:10.17780/ksujes.1493906

TR EN

GÜNEŞ PANELLERİ ÜZERİNDEKİ TOZUN DERİN ÖĞRENME İLE TESPİTİ

Abstract

Güneş enerjisi, temiz ve sürdürülebilir bir enerji kaynağı olarak çevre dostu bir alternatiftir. Bu enerji kaynağının yaygınlaşması, hem çevre hem de ekonomik açıdan büyük faydalar sunar. Ancak, güneş panellerinin verimliliğini etkileyen bazı unsurlar vardır. Bu unsurlardan biri de tozdur. Toz, güneş panellerinin yüzeyine biriktiğinde enerji üretim verimliliğini önemli ölçüde düşürebilir. Bu nedenle, güneş panellerindeki tozun tespiti ve hızlı bir şekilde temizlenmesi büyük önem taşır. Özellikle geniş alanlarda bu sürecin insansız yapay zekâ sistemleriyle yönetilmesi, hem zaman hem de maliyet açısından önemli avantajlar sağlayacaktır. Son yıllarda evrişimsel sinir ağları görüntü sınıflandırma konusunda önemli başarılar elde etmiştir. Özellikle transfer öğrenme yöntemleri bu alanda başarısını kanıtlamıştır. Bu çalışmada, kirli ve temiz güneş panellerini sınıflandırmak için önceden eğitilmiş derin öğrenme modellerini (EfficientNetB3, ResNet50, MobileNet, VGG19, Xception, InceptionResNetV2, VGG16, ResNet101, DenseNet201, EfficientNetB7) kullanarak sınırlı veri ile yeni bir görevi çözme amaçlanmaktadır. Bu modellerin seçilme nedeni, her birinin farklı güçlü yönlere sahip olması ve çeşitli görevlerde başarılı performans sergilemiş olmalarıdır. Bu modeller arasında en iyi performansa sahip modeller, sınıflandırma tahminini iyileştirmek için birleştirilmiştir. Önerilen topluluk öğrenme yaklaşımı, modellerin tahmin sonuçlarını oylama yaklaşımıyla ele alarak %99,31 sınıflandırma doğruluğuna ulaşmıştır. Sonuç olarak, bu yaklaşım güneş enerjisi sistemlerinin bakım süreçlerini optimize etmeyi, enerji verimliliğini artırmayı ve uzun vadede sürdürülebilir enerji kullanımını desteklemeyi amaçlamaktadır.

Keywords

DETECTION OF DUST ON SOLAR PANELS WITH DEEP LEARNING

Abstract

Solar 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.

Keywords

Thanks

This article is based on Tuba SEFER's Master's thesis entitled “Dust detection on solar panels with deep learning”, conducted under the supervision of Dr. Mahmut KAYA at Siirt University, Institute of Science and Technology, Department of Computer Engineering.

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Details

Primary Language

English

Subjects

Deep Learning, Neural Networks

Journal Section

Research Article

Authors

Tuba Sefer
0009-0002-5650-1683
Türkiye

Mahmut Kaya ^*
0000-0002-7846-1769
Türkiye

Publication Date

December 3, 2024

Submission Date

June 1, 2024

Acceptance Date

July 19, 2024

Published in Issue

Year 2024 Volume: 27 Number: 4

DOI

https://doi.org/10.17780/ksujes.1493906

IZ

https://izlik.org/JA88BL47EA

Cite

RIS / Bibtex

APA

Sefer, T., & Kaya, M. (2024). DETECTION OF DUST ON SOLAR PANELS WITH DEEP LEARNING. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 27(4), 1451-1464. https://doi.org/10.17780/ksujes.1493906

AMA

1.Sefer T, Kaya M. DETECTION OF DUST ON SOLAR PANELS WITH DEEP LEARNING. KSU J. Eng. Sci. 2024;27(4):1451-1464. doi:10.17780/ksujes.1493906

Chicago

Sefer, Tuba, and Mahmut Kaya. 2024. “DETECTION OF DUST ON SOLAR PANELS WITH DEEP LEARNING”. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi 27 (4): 1451-64. https://doi.org/10.17780/ksujes.1493906.

EndNote

Sefer T, Kaya M (December 1, 2024) DETECTION OF DUST ON SOLAR PANELS WITH DEEP LEARNING. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi 27 4 1451–1464.

IEEE

[1]T. Sefer and M. Kaya, “DETECTION OF DUST ON SOLAR PANELS WITH DEEP LEARNING”, KSU J. Eng. Sci., vol. 27, no. 4, pp. 1451–1464, Dec. 2024, doi: 10.17780/ksujes.1493906.

ISNAD

Sefer, Tuba - Kaya, Mahmut. “DETECTION OF DUST ON SOLAR PANELS WITH DEEP LEARNING”. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi 27/4 (December 1, 2024): 1451-1464. https://doi.org/10.17780/ksujes.1493906.

JAMA

1.Sefer T, Kaya M. DETECTION OF DUST ON SOLAR PANELS WITH DEEP LEARNING. KSU J. Eng. Sci. 2024;27:1451–1464.

MLA

Sefer, Tuba, and Mahmut Kaya. “DETECTION OF DUST ON SOLAR PANELS WITH DEEP LEARNING”. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, vol. 27, no. 4, Dec. 2024, pp. 1451-64, doi:10.17780/ksujes.1493906.

Vancouver

1.Tuba Sefer, Mahmut Kaya. DETECTION OF DUST ON SOLAR PANELS WITH DEEP LEARNING. KSU J. Eng. Sci. 2024 Dec. 1;27(4):1451-64. doi:10.17780/ksujes.1493906