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

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

Statement of the Use of Generative AI and AI-Assisted Technologies in the Writing Process

During the preparation of this manuscript, the authors used ChatGPT (OpenAI) to enhance readability and improve the academic language of the text, as well as to assist in the conceptual design of the graphical abstract. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the published article.

Acknowledgments

The author thanks the School of Tea and Coffee and the Yunnan International Joint Laboratory of Digital Conservation and Germplasm Innovation at Pu’er University for providing institutional support. The authors also acknowledge the constructive academic environment and institutional support that facilitated the completion of this study.

Author Contributions

Conceptualization: A.A.A.O.; Methodology: A.A.A.O., O.M.M.A.; Software: A.A.A.O.; Validation: A.A.A.O., I.I.M.I., M.I.A.B.; Formal Analysis: A.A.A.O.; Investigation: A.A.A.O., O.M.M.A.; Resources: I.I.M.I., M.I.A.B.; Data Curation: A.A.A.O.; Writing—Original Draft Preparation: A.A.A.O.; Writing—Review & Editing: A.A.A.O., I.I.M.I., M.I.A.B., O.M.M.A.; Visualization: A.A.A.O., O.M.M.A.

Ethics Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon request.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or non-profit sectors.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper.

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