Sustainable Applications and Innovations in Energy Transfer Processes and Photovoltaic Performance

Dear Colleagues,

As global energy demands continue to rise and the push towards sustainable solutions intensifies, the integration of advanced computational technologies in energy systems is becoming increasingly crucial. This integration not only improves energy efficiency but also ensures safety and reliability in energy production and management. Photovoltaic systems are at the forefront of this transformation, benefiting significantly from advancements in AI, machine learning, and image processing to optimize performance and predict energy yields.

This Special Issue, titled “Sustainable Applications and Innovation in Energy Transfer Processes and Photovoltaic Performance”, is designed to facilitate multidisciplinary research that integrates cutting-edge technologies such as artificial intelligence, machine learning, and computer vision into sustainable energy practices. With a broad scope covering energy transfer and photovoltaic systems, we invite contributions that harness advanced computational techniques and innovative methodologies to enhance both the efficiency and safety of these systems.

This Special Issue is dedicated to exploring how innovative technologies can be leveraged to advance the performance and integration of photovoltaic systems within sustainable energy frameworks. We particularly emphasize the role of computer vision, pattern recognition, and artificial intelligence in enhancing the predictive accuracy and operational efficiency of these systems. Moreover, this issue will delve into the application of federated learning in the operation of photovoltaic systems, examining how this collaborative machine learning approach can optimize performance and enhance data privacy across distributed energy networks.

For this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

• Federated learning for optimizing photovoltaic system performance and privacy-preserving energy data management;
• Applications of AI and machine learning in enhancing the efficiency and reliability of photovoltaic systems;
• The role of computer vision and image processing in real-time monitoring and diagnostic of energy systems;
• Integration of pattern recognition and AI in predictive maintenance to increase energy transfer efficiency;
• Innovative uses of artificial intelligence to improve system integration and operational safety in sustainable energy environments.

We invite submissions of original research articles, comprehensive reviews, and case studies that address these themes. Contributions should not only showcase technological advancements but also discuss their practical implications and potential to drive sustainable energy solutions forward.

Through this Special Issue, we aim to assemble a collection of high-quality research that provides valuable insights into the integration of artificial intelligence and related technologies in the field of energy. We look forward to receiving valuable submissions, which will undoubtedly contribute to the advancement of sustainable practices in the energy sector.

Dr. Jihoon Moon
Dr. Byeongjoon Noh
Dr. Fath U Min Ullah
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. Sustainability 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 2400 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.

• federated learning
• photovoltaic system performance
• privacy-preserving energy data
• artificial intelligence
• machine learning
• computer vision
• image processing
• predictive maintenance
• energy transfer efficiency
• sustainable energy technology