Advanced Artificial Intelligence for Photovoltaic Energy Systems
A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A2: Solar Energy and Photovoltaic Systems".
Deadline for manuscript submissions: 20 June 2026 | Viewed by 10
Special Issue Editors
2. Solar Energy Engineering Program, Department of Sustainable Systems Engineering (INATECH), Albert Ludwigs University of Freiburg, 79110 Freiburg, Germany
Interests: energy transition; renewable energy; photovoltaics; smart grid; enabling technologies; artificial intelligence (AI); digital twin, unmanned aerial vehicle (UAV)
Special Issues, Collections and Topics in MDPI journals
Interests: energy systems; artificial intelligence; photovoltaic; power systems protection; condition monitoring
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The rapid global expansion of photovoltaic (PV) systems has created an urgent need for intelligent, data-driven approaches to optimize performance, reliability, and cost-effectiveness. Recent breakthroughs in artificial intelligence—especially deep learning, reinforcement learning, edge AI, and hybrid physical–data models—have created new opportunities for advanced monitoring, forecasting, diagnostics, control, and optimization across all scales of PV energy systems.
This Special Issue aims to present cutting-edge research, innovative methodologies, and real-world applications of AI in the PV domain. We are seeking original research articles, reviews, case studies, and technical developments that advance the integration of intelligent algorithms into PV system modeling, operation, and management.
Topics of interest include (but are not limited to)
- AI-driven solar irradiance and power forecasting.
- Deep learning for PV performance modeling.
- Fault diagnosis, fault detection, and predictive maintenance using AI.
- Reinforcement learning and intelligent control for PV systems.
- AI for PV–grid interaction and stability.
- Digital twins for PV power plants.
- Hybrid physical–AI models for PV output prediction.
- Edge AI, embedded intelligence, and IoT for PV monitoring.
- Data analytics for PV degradation assessment.
- AI-guided PV system sizing, design, and optimization.
- Uncertainty quantification, probabilistic AI models, and risk analysis.
- AI in PV storage systems, hybrid microgrids, and energy management.
- Applications of generative AI and foundation models in renewable energy.
- Trustworthy, interpretable, and physics-informed AI for PV systems.
We are particularly interested in contributions that address practical implementations, real-world datasets, or new AI methodologies with measurable improvements in PV system performance.
Prof. Dr. Mohammadreza Aghaei
Dr. Aref Eskandari
Guest Editors
Manuscript Submission Information
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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.
Keywords
- artificial intelligence (AI)
- deep learning
- solar photovoltaics (PV)
- power forecasting
- fault detection and diagnosis
- reinforcement learning
- digital twin
- edge AI / IoT
- PV system optimization
- renewable energy analytics
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