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Artificial Intelligence in Photovoltaic Power Systems: A Bibliometric and Thematic Analysis of Knowledge Structures, Research Evolution, and Emerging Directions Toward Sustainable Energy Systems

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Artificial Intelligence in Photovoltaic Power Systems: A Bibliometric and Thematic Analysis of Knowledge Structures, Research Evolution, and Emerging Directions Toward Sustainable Energy Systems

Author Information
1
School of Tea and Coffee, Puer University, Puer 665000, China
2
Yunnan International Joint Laboratory of Digital Conservation and Germplasm Innovation and Application of China–Laos Tea Resources, Puer University, Puer 665000, China
3
Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming 650500, China
4
College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
5
Faculty of Metallurgical and Energy Engineering, Kunming University of Science and Technology, Kunming 650093, China
*
Authors to whom correspondence should be addressed.

Received: 27 January 2026 Revised: 24 February 2026 Accepted: 19 March 2026 Published: 27 March 2026

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© 2026 The authors. This is an open access article under the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).

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Clean Energy Sustain. 2026, 4(1), 10005; DOI: 10.70322/ces.2026.10005
ABSTRACT: Artificial intelligence (AI) has rapidly become a core enabling technology in photovoltaic (PV) power systems, supporting improvements in forecasting accuracy, operational control, fault diagnosis, and system-level energy management. Despite the rapid growth of this field, a comprehensive understanding of its intellectual structure, thematic evolution, and emerging methodological directions remains fragmented. To address this gap, this study develops an integrated bibliometric-thematic analysis framework to systematically map the knowledge structure, research trajectories, and methodological frontiers of AI applications in PV power systems. The analysis is based on 4752 peer-reviewed journal articles indexed in Scopus (2006–2025). It combines performance analysis, co-citation analysis, keyword co-occurrence analysis, and bibliographic coupling to answer five structured research questions. The results demonstrate that PV power forecasting constitutes the central intellectual backbone of AI-based PV research, with the highest citation concentration and the strongest thematic connectivity across clusters. Thematic evolution analysis reveals a clear methodological transition from conventional machine learning models toward hybrid deep learning architectures, uncertainty-aware prediction frameworks, and physics-based AI integration. Furthermore, emerging research frontiers are characterized by generative learning models, multi-source data fusion strategies, and resilience-oriented fault diagnostics, while critical gaps persist in benchmarking standardization, uncertainty quantification, system-level integration, and large-scale industrial deployment. Unlike prior reviews that focus on isolated technical applications, this study provides the first integrated performance analysis and science-mapping synthesis that connects intellectual foundations, thematic evolution, and frontier innovations across the entire AI-based PV ecosystem. The findings offer a structured research roadmap and actionable guidance for researchers, PV plant operators, and policymakers aiming to design intelligent, scalable, and resilient PV energy systems that support the global low-carbon transition.
Keywords: Artificial intelligence; Photovoltaic power systems; Machine learning; Deep learning; Power forecasting; Intelligent control; Fault diagnosis; Bibliometric-thematic analysis

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