Enhancing solar photovoltaic modules quality assurance through convolutional neural network-aided automated defect detection

Hassan, Sharmarke and Dhimish, Mahmoud (2023) Enhancing solar photovoltaic modules quality assurance through convolutional neural network-aided automated defect detection. Renewable Energy. 119389. ISSN 0960-1481

Abstract

Metadata

Item Type: Article
Authors/Creators:
  • Hassan, Sharmarke
  • Dhimish, Mahmoud (md1580@york.ac.uk)
Copyright, Publisher and Additional Information:

This is an author-produced version of the published paper. Uploaded in accordance with the University’s Research Publications and Open Access policy.

Keywords: Renewable energy,Solar Energy,Photovoltaic,Artificial intelligence,Machine learning,Convolutional neural network
Dates:
  • Published: 1 December 2023
  • Published (online): 9 October 2023
  • Accepted: 30 September 2023
Institution: The University of York
Academic Units: The University of York > Faculty of Sciences (York) > Electronic Engineering (York)
Depositing User: Pure (York)
Date Deposited: 13 Oct 2023 23:20
Last Modified: 14 Mar 2025 00:10
Published Version: https://doi.org/10.1016/j.renene.2023.119389
Status: Published
Refereed: Yes
Identification Number: 10.1016/j.renene.2023.119389
Open Archives Initiative ID (OAI ID):

Download

Filename: Manuscript_author_accepted_version.pdf

Description: Manuscript author accepted version

Licence: CC-BY 2.5

Export

Statistics