Algorithmic Approaches and Artificial Intelligence in Photovoltaic Systems
A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".
Deadline for manuscript submissions: 30 April 2026 | Viewed by 45
Special Issue Editor
Interests: photovoltaics; multi-junction iii-v compound solar cells; organic/polymer solar cells; quantum dot solar cells; perovskite solar cells; machine learning; deep learning; artificial intelligence
Special Issue Information
Dear Colleagues,
The rapid growth of artificial intelligence (AI) and algorithm optimization has changed the field of photovoltaic (PV) energy systems. As the world pushes for sustainable and carbon-neutral energy, AI-driven algorithms are becoming essential tools for improving the efficiency, reliability, and intelligence of PV systems. This applies to everything from material design and power forecasting to fault detection, control, and grid integration.
Recent advancements in deep learning, reinforcement learning, evolutionary computing, and optimization algorithms have revealed new ways to harvest and manage solar energy. These methods enable breakthroughs in areas like real-time energy forecasting, adaptive maximum power point tracking (MPPT), predictive maintenance, degradation modeling, and smart control of hybrid renewable systems. In addition, AI-based analytical frameworks are increasingly used for performance prediction, fault diagnosis, and autonomous decision making in large solar farms and microgrids.
This Special Issue offers a space for researchers and practitioners to share new methods, frameworks, and applications of AI and algorithm strategies in photovoltaic systems. We especially welcome contributions that focus on innovative algorithms, hybrid modeling techniques, data-driven system optimization, and real-world applications.
The topics include, but are not limited to, the following:
- Machine learning and deep learning for PV performance prediction;
- Reinforcement learning and control strategies for MPPT and grid integration;
- Evolutionary and swarm intelligence optimization for PV system design;
- AI-based energy forecasting and load management;
- Fault detection, diagnosis, and predictive maintenance using AI;
- AI-assisted material discovery and photovoltaic device optimization;
- Hybrid renewable energy systems incorporating PV and AI control;
- Edge intelligence and IoT for real-time solar energy management;
- Explainable and interpretable AI models for PV applications.
Prof. Dr. Simon Foo
Guest Editor
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 250 words) can be sent to the Editorial Office for assessment.
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. Algorithms is an international peer-reviewed open access monthly 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 1800 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.
Keywords
- photovoltaic systems
- machine learning
- deep learning
- swarm intelligence optimization
- fault detection
- explainable and interpretable AI
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