Artificial Intelligence in Photovoltaic Systems: Advanced Modeling, Optimization, Forecasting, and Fault Diagnosis

Dear Colleagues,

The global shift toward clean and sustainable energy has underscored the strategic role of photovoltaic (PV) systems in modern power infrastructure. As PV technologies expand across utility-scale, commercial, and residential sectors, ensuring efficient and reliable operation under dynamic real-world conditions has become increasingly critical. These systems face various challenges, including environmental variability, performance degradation, power generation uncertainty, and operational faults. Addressing these complexities necessitates the development of intelligent, adaptive, and autonomous solutions. In this context, Artificial Intelligence (AI) has emerged as a transformative enabler, driving innovation across the PV system lifecycle—from predictive modeling and control to fault diagnosis and system optimization. This Special Issue aims to showcase cutting-edge research integrating AI methodologies, such as machine learning, deep learning, reinforcement learning, fuzzy logic, and expert systems, in designing, monitoring, managing, and enhancing PV systems.

By leveraging data-driven techniques, researchers are developing intelligent control architectures, accurate forecasting models, and robust diagnostic tools that collectively improve solar power systems' efficiency, resilience, and lifespan. This Special Issue focuses on advanced AI-driven strategies that transcend traditional engineering practices, contributing to more effective energy management, reduced operational costs, and enhanced system performance, particularly under variable environmental conditions. Further, the Issue will explore the integration of AI in hybrid energy systems, IoT-based PV monitoring, real-time optimization, and seamless interaction with smart grids. It seeks to bridge theoretical advances with practical applications, fostering interdisciplinary collaboration between academia and industry.

We welcome original research articles, comprehensive reviews, and case studies presenting novel theoretical insights, algorithmic developments, experimental validations, and real-world implementations. Contributions from power electronics, control systems, computer science, and energy engineering are particularly encouraged to support the advancement of intelligent and autonomous PV technologies in the pursuit of a sustainable energy future.

Topics of interest include, but are not limited to:

• AI-based modeling and performance prediction;
• Predicting photovoltaic (PV) performance;
• AI-driven system optimization and control;
• AI-enabled solar forecasting;
• Intelligent fault detection and diagnosis;
• Real-time monitoring and predictive maintenance;
• IoT and smart grid integration for PV systems;
• Hybrid and multi-agent AI approaches;
• AI for energy storage and microgrid management;
• AI applications in the sustainable energy transition.

We look forward to considering your submissions.

Prof. Dr. Gang Xiong
Dr. Ehtisham Lodhi
Guest Editors

Manuscript Submission Information

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• artificial intelligence (AI)
• photovoltaic systems (PV)
• solar energy
• performance prediction
• fault detection
• predictive maintenance
• maximum power point tracking (MPPT)
• PV system optimization
• solar forecasting
• energy storage
• microgrid management
• smart grid integration
• IoT in renewable energy
• hybrid AI models
• multi-agent systems
• data-driven control
• sustainable energy transition