太陽光パネルのマルチクラス故障検出のための事前学習CNNモデルの比較研究
Comparative study of pre-trained CNN models for multiclass fault detection in solar panels (原題)
Karli Eka Setiawan, Marvel Martawidjaja, Hayyun Lisdiana
🤖 gxceed AI 要約
日本語
本研究は、太陽光パネルの故障を自動検出するためのCNNベースの画像分類手法を提案する。Kaggleの公開データセットを用いて6クラスの分類を行い、Inception-V3が91%の精度で最も優れていた。深層学習による太陽光パネルの保守・監視システムの実用化可能性を示す。
English
This study proposes a CNN-based image classification approach to automatically detect faults in solar panels. Using a public Kaggle dataset with six classes, Inception-V3 achieved the highest accuracy of 91% and best F1-scores in four categories. The results confirm the potential of deep learning for enhancing solar panel maintenance and monitoring systems.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本の再生可能エネルギー拡大に伴い、太陽光発電所の維持管理の効率化が課題となっている。本手法は、点検の自動化によるコスト削減と発電効率の向上に寄与し、FIT制度後の自立化に向けた運用最適化に貢献する可能性がある。
In the global GX context
As solar energy expands globally, automated fault detection is crucial for efficient O&M. This study demonstrates a practical deep learning application for solar panel monitoring, aligning with the global push for renewable energy optimization and climate change mitigation.
👥 読者別の含意
🔬研究者:Provides a comparative evaluation of pre-trained CNNs for solar panel fault detection, offering a baseline for further research in renewable energy AI applications.
🏢実務担当者:Offers a viable automated inspection solution for solar panel maintenance, potentially reducing manual inspection costs and improving operational efficiency.
🏛政策担当者:Highlights the role of AI in supporting renewable energy infrastructure, which can inform policies promoting smart grid and clean energy technologies.
📄 Abstract（原文）
Solar power as renewable energy can be an alternative to fossil-based power where it is carbonless, environmentally friendly, and combats climate change. In solar panel systems, manual assessment by personnel is timedemanding and prone to human error, necessitating automated solutions. The implementation of smart systems that can automatically detect objects that hinder the solar panel from receiving solar energy can be very helpful in reducing the potential threat of decreasing performance in power generation. This study proposes a convolutional neural networks (CNN)-based image classification approach to automatically identify common solar panel conditions using visual data. The dataset used was a public dataset titled “Solar Panel Images: Clean and Faulty Images”, obtained from Kaggle, containing six classes for multiclass classification. The most effective pretrained CNN-based deep learning model for uncovering issues in solar panels was inception-V3, achieving an overall accuracy of 91% and the highest F1-score in four categories: clean, electrical damage, physical damage, and snow coverage. These outcomes confirm the potential of implementing deep learning image classification for enhancing solar panel maintenance and monitoring systems in real-world applications.
🔗 Provenance — このレコードを発見したソース
- Zenodo https://zenodo.org/records/22240574first seen 2026-09-02 04:24:49 · last seen 2026-09-03 04:37:38
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