PV module power prediction by deep learning on electroluminescence images - Assessing the physics learned by a convolutional neural network-Reference-Cited by-同舟云学术

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PV module power prediction by deep learning on electroluminescence images - Assessing the physics learned by a convolutional neural network

Published:2024-01 Issue: Volume:264 Page:112621
ISSN:0927-0248
Container-title:Solar Energy Materials and Solar Cells
language:en
Short-container-title:Solar Energy Materials and Solar Cells

Author:

Lüer Larry,Forberich Karen^ORCID,Hepp Johannes,Buerhop-Lutz Claudia,Winkler Thilo,Rodrigues Sandy,Hoffmann Mathis,Doll Bernd,Hauch Jens^ORCID,Brabec Christoph J.,Peters Ian Marius^ORCID

Funder

Bayerisches Staatsministerium fur Wirtschaft Landesentwicklung und Energie

Bundesministerium für Wirtschaft und Klimaschutz

Publisher

Elsevier BV

Subject

Surfaces, Coatings and Films,Renewable Energy, Sustainability and the Environment,Electronic, Optical and Magnetic Materials

Reference25 articles.

1. Drone-based daylight electroluminescence imaging of PV modules;Alves Dos Reis Benatto;IEEE J. Photovoltaics,2020

2. Deep learning-based pipeline for module power prediction from EL measurements;Hoffmann;Prog. Photovoltaics Res. Appl.,2020

3. Automated pipeline for photovoltaic module electroluminescence image processing and degradation feature classificatio;Karimi;IEEE J. Photovoltaics,2019

4. Fault Detection in aerial images of photovoltaic modules based on Deep learning;Venkatesh,2021

5. Pa M, Kazemi A, A Fault Detection Scheme Utilizing Convolutional Neural Network for PV Solar Panels with High Accuracy https://doi.org/10.48550/arXiv.2210.09226.

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