Redefining failure detection in PV Systems: a comparative study of GPT-4o and ResNet's computer vision in aerial infrared imagery analysis
The rapid growth of the solar photovoltaic industry underlines the importance of effective operation and maintenance strategies, particularly for large-scale systems. Aerial infrared thermography has become an essential tool for detecting anomalies in photovoltaic modules due to its cost-effectivene...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | EPJ Photovoltaics |
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| Online Access: | https://www.epj-pv.org/articles/epjpv/full_html/2025/01/pv20240079/pv20240079.html |
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| author | Gallmetzer Sandra Sondoqah Mousa Turri Evelyn Koester Lukas Louwen Atse Moser David |
| author_facet | Gallmetzer Sandra Sondoqah Mousa Turri Evelyn Koester Lukas Louwen Atse Moser David |
| author_sort | Gallmetzer Sandra |
| collection | DOAJ |
| description | The rapid growth of the solar photovoltaic industry underlines the importance of effective operation and maintenance strategies, particularly for large-scale systems. Aerial infrared thermography has become an essential tool for detecting anomalies in photovoltaic modules due to its cost-effectiveness and scalability. Continuous monitoring through advanced fault detection and classification methods can maintain optimal system performance and extend the life of PV modules. This study investigates the application of advanced artificial intelligence methods for fault detection and classification comparing the performance of GPT-4o, a multimodal large language model, and ResNet, a convolutional neural network renowned for image classification tasks. Our research evaluates the effectiveness of both models using infrared images, focusing on binary defect detection and multiclass classification. ResNet demonstrated advantages in terms of computational efficiency and ease of implementation. Conversely, GPT-4o offered superior adaptability and interpretability, effectively analysing multimodal data to identify and explain subtle anomalies in thermal imagery. However, its higher computational requirements limit its feasibility in resource-limited settings. The results highlight the complementary strengths of these models and provide valuable insights into their role in advancing automated fault diagnosis in photovoltaic systems. |
| format | Article |
| id | doaj-art-834934d2f0034d4ca577ecefd05771ea |
| institution | DOAJ |
| issn | 2105-0716 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | EPJ Photovoltaics |
| spelling | doaj-art-834934d2f0034d4ca577ecefd05771ea2025-08-20T03:08:47ZengEDP SciencesEPJ Photovoltaics2105-07162025-01-01162310.1051/epjpv/2025010pv20240079Redefining failure detection in PV Systems: a comparative study of GPT-4o and ResNet's computer vision in aerial infrared imagery analysisGallmetzer Sandra0https://orcid.org/0000-0002-6660-6115Sondoqah Mousa1Turri Evelyn2https://orcid.org/0009-0005-5668-0839Koester Lukas3Louwen Atse4Moser David5Institute for Renewable Energy, Eurac ResearchInstitute for Renewable Energy, Eurac ResearchInstitute for Renewable Energy, Eurac ResearchInstitute for Renewable Energy, Eurac ResearchInstitute for Renewable Energy, Eurac ResearchInstitute for Renewable Energy, Eurac ResearchThe rapid growth of the solar photovoltaic industry underlines the importance of effective operation and maintenance strategies, particularly for large-scale systems. Aerial infrared thermography has become an essential tool for detecting anomalies in photovoltaic modules due to its cost-effectiveness and scalability. Continuous monitoring through advanced fault detection and classification methods can maintain optimal system performance and extend the life of PV modules. This study investigates the application of advanced artificial intelligence methods for fault detection and classification comparing the performance of GPT-4o, a multimodal large language model, and ResNet, a convolutional neural network renowned for image classification tasks. Our research evaluates the effectiveness of both models using infrared images, focusing on binary defect detection and multiclass classification. ResNet demonstrated advantages in terms of computational efficiency and ease of implementation. Conversely, GPT-4o offered superior adaptability and interpretability, effectively analysing multimodal data to identify and explain subtle anomalies in thermal imagery. However, its higher computational requirements limit its feasibility in resource-limited settings. The results highlight the complementary strengths of these models and provide valuable insights into their role in advancing automated fault diagnosis in photovoltaic systems.https://www.epj-pv.org/articles/epjpv/full_html/2025/01/pv20240079/pv20240079.htmlinspectionperformancefailure detection and classificationphotovoltaicsmultimodal large language modelsprompt engineering |
| spellingShingle | Gallmetzer Sandra Sondoqah Mousa Turri Evelyn Koester Lukas Louwen Atse Moser David Redefining failure detection in PV Systems: a comparative study of GPT-4o and ResNet's computer vision in aerial infrared imagery analysis EPJ Photovoltaics inspection performance failure detection and classification photovoltaics multimodal large language models prompt engineering |
| title | Redefining failure detection in PV Systems: a comparative study of GPT-4o and ResNet's computer vision in aerial infrared imagery analysis |
| title_full | Redefining failure detection in PV Systems: a comparative study of GPT-4o and ResNet's computer vision in aerial infrared imagery analysis |
| title_fullStr | Redefining failure detection in PV Systems: a comparative study of GPT-4o and ResNet's computer vision in aerial infrared imagery analysis |
| title_full_unstemmed | Redefining failure detection in PV Systems: a comparative study of GPT-4o and ResNet's computer vision in aerial infrared imagery analysis |
| title_short | Redefining failure detection in PV Systems: a comparative study of GPT-4o and ResNet's computer vision in aerial infrared imagery analysis |
| title_sort | redefining failure detection in pv systems a comparative study of gpt 4o and resnet s computer vision in aerial infrared imagery analysis |
| topic | inspection performance failure detection and classification photovoltaics multimodal large language models prompt engineering |
| url | https://www.epj-pv.org/articles/epjpv/full_html/2025/01/pv20240079/pv20240079.html |
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