HAVA GÖRÜNTÜLERİNİN DERİN ÖĞRENMESİ KULLANILARAK DAĞINIK FOTOVOLTAİK PİLLERİN OTOMATİK HARİTALANMASI VE SEGMENTASYONUHAVA GÖRÜNTÜLERİNİN DERİN ÖĞRENMESİ KULLANILARAK DAĞINIK FOTOVOLTAİK PİLLERİN OTOMATİK HARİTALANMASI VE SEGMENTASYONU

Sinan Altun

doi:10.17780/ksujes.1617092

EN TR

AUTOMATIC MAPPING AND SEGMENTATION OF SCATTERED PHOTOVOLTAIC BATTERIES USING DEEP LEARNING OF AERIAL IMAGERY

Abstract

Among solar energy systems, the generation of electrical energy through photovoltaic cells has become a widespread trend worldwide. The fact that solar energy is considered as an unlimited resource and the high greenhouse gas emissions of conventional power plants do not constitute an obstacle in electricity generation with photovoltaic cells makes this method very attractive today, when global warming is a threat. In this study, distributed photovoltaic systems in three leading metropolises of Turkey, Istanbul, Ankara, and Izmir, are analysed through aerial imagery. The investigation process was carried out using deep learning techniques. To the best of our knowledge, this is the first research in this field in Turkey. Since there is no dataset containing aerial images of photovoltaic systems in the country, Google Earth platform was used to create the test dataset. The aim of the study is to investigate the growth potential of the solar energy systems market in Turkey by using aerial photographs, which have attracted worldwide attention. The classification and segmentation results obtained are successful and reveal that solar energy systems market analysis can be made for Turkey with similar aerial images around the world. Classification scores: AUC value is 0.9 for AlexNet, 0.87 for GoogLeNet, and 0.83 for Inception.

Keywords

HAVA GÖRÜNTÜLERİNİN DERİN ÖĞRENMESİ KULLANILARAK DAĞINIK FOTOVOLTAİK PİLLERİN OTOMATİK HARİTALANMASI VE SEGMENTASYONUHAVA GÖRÜNTÜLERİNİN DERİN ÖĞRENMESİ KULLANILARAK DAĞINIK FOTOVOLTAİK PİLLERİN OTOMATİK HARİTALANMASI VE SEGMENTASYONU

Öz

Güneş enerjisi sistemleri arasında fotovoltaik piller aracılığıyla elektrik enerjisi üretimi, dünya genelinde yaygın bir eğilim haline gelmiştir. Güneş enerjisinin sınırsız bir kaynak olarak değerlendirilmesi ve geleneksel enerji santrallerinin yüksek sera gazı emisyonlarının fotovoltaik pillerle elektrik üretiminde bir engel teşkil etmemesi, küresel ısınmanın tehdit oluşturduğu günümüzde bu yöntemi oldukça cazip kılmaktadır. Bu çalışmada, Türkiye'nin önde gelen üç metropolü olan İstanbul, Ankara ve İzmir'deki dağıtık fotovoltaik sistemler, hava görüntüleri aracılığıyla incelenmiştir. İnceleme süreci, derin öğrenme teknikleri kullanılarak gerçekleştirilmiştir. Bilgilerimize göre, Türkiye'de bu alanda gerçekleştirilen ilk araştırmadır. Ülkede fotovoltaik sistemlere dair hava görüntülerini içeren bir veri seti bulunmadığı için, test veri setinin oluşturulmasında Google Earth platformu kullanılmıştır. Çalışmanın amacı, dünya genelinde ilgi gören hava fotoğraflarını kullanarak Türkiye'de güneş enerjisi sistemleri pazarının büyüme potansiyelini araştırmaktır. Elde edilen sınıflandırma ve segmentasyon sonuçları başarılı olup, dünya genelindeki benzer hava görüntüleri ile Türkiye için güneş enerjisi sistemleri pazar analizi yapılabileceğini ortaya koymaktadır. Sınıflandırma skorları: AlexNet AUC skoru 0.9, GoogLeNet 0.87 ve Inception için 0.83

Anahtar Kelimeler

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Details

Primary Language

Turkish

Subjects

Image Processing , Deep Learning , Photovoltaic Power Systems

Journal Section

Research Article

Authors

Sinan Altun ^*
0000-0002-2356-0460
Türkiye

Publication Date

June 3, 2025

Submission Date

January 10, 2025

Acceptance Date

April 22, 2025

Published in Issue

Year 2025 Volume: 28 Number: 2

DOI

https://doi.org/10.17780/ksujes.1617092

IZ

https://izlik.org/JA94SD23LB

Cite

RIS / Bibtex

APA

Altun, S. (2025). HAVA GÖRÜNTÜLERİNİN DERİN ÖĞRENMESİ KULLANILARAK DAĞINIK FOTOVOLTAİK PİLLERİN OTOMATİK HARİTALANMASI VE SEGMENTASYONUHAVA GÖRÜNTÜLERİNİN DERİN ÖĞRENMESİ KULLANILARAK DAĞINIK FOTOVOLTAİK PİLLERİN OTOMATİK HARİTALANMASI VE SEGMENTASYONU. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 28(2), 835-850. https://doi.org/10.17780/ksujes.1617092