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Advances in Artificial Intelligence for Photovoltaic Research and Applications

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F5: Artificial Intelligence and Smart Energy".

Deadline for manuscript submissions: 15 October 2024 | Viewed by 67

Special Issue Editors

E-Mail Website
Guest Editor
Florida Solar Energy Center, University of Central Florida, Cocoa, FL 32922, USA
Interests: photovoltaics; artificial intelligence; multiscale multiphysics modeling; nanotechnology

E-Mail Website
Guest Editor
Materials Science and Engineering, Florida Solar Energy Center, University of Central Florida, Cocoa, FL 32922, USA
Interests: photovoltaics; semiconductors; optical materials; electronic materials

Special Issue Information

Dear Colleagues,

The aim and scope of this Special Issue titled “Advances in Artificial Intelligence for Photovoltaic Research and Applications” are to present the recent developments in machine learning models applied to photovoltaic life stages: materials, manufacturing, field operation, maintenance, forecasting and recycling. The models should be validated on real-world data typically collected from photovoltaic materials, devices and systems including time-series data, current–voltage characteristic curves, geospatial data, sequential data, infrared images, electroluminescence images, photoluminescence images, ultraviolet fluorescence images, operation and maintenance records or any other data associated with photovoltaic materials, cost, performance, reliability, degradation, failure or weather. The model objectives of particular interest include object detection, instance segmentation, classification, imputation, prediction, clustering, anomaly detection, generation and noise reduction. We encourage you to share your datasets with the research community.

This Special Issue is open to both original research articles covering progress in machine learning techniques and architectures including but not limited to the following:

  • Linear models;
  • Clustering models;
  • Decision tree models;
  • Dimensionality reduction models;
  • Deep learning models;
  • Computer vision models;
  • Physics-based models.
  • Generative models;
  • Transformer models;
  • Natural language processing models;
  • Ensemble models;
  • Foundation models.

Dr. Hubert Seigneur
Dr. Kristopher Davis
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


  • artificial intelligence
  • machine learning
  • photovoltaics
  • solar cells
  • solar panels
  • faults
  • anomaly
  • degradation. failure
  • time series
  • current–voltage curves
  • geospatial location
  • infrared images
  • electroluminescence images
  • photoluminescence images
  • ultraviolet fluorescence images
  • material characterization

Published Papers

This special issue is now open for submission.
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