Energy Security of Photovoltaic Systems in the Context of Artificial Intelligence Development

Tomasz Grudniewski, Zbigniew Ciekanowski, Slawomir Żurawski, Valeriy Kuznetsov, Petro Hubskyi and Wlodzimierz Kruczek

European Research Studies Journal, 2025, vol. XXVIII, issue 3, 786-806

Abstract: Purpose: This article analyzes the impact of artificial intelligence (AI) advancements on the energy and operational security of photovoltaic (PV) systems. The rapid expansion of solar energy brings numerous benefits but also introduces new challenges, including production instability and operational risks. The paper highlights key AI applications in PV energy forecasting, smart energy management, and advanced fault diagnostics. It also identifies challenges associated with AI deployment, such as data quality, cybersecurity threats, and regulatory issues. Design/Methodology/Approach: The study adopts a mixed-method approach, combining a systematic literature review with case studies of AI applications in photovoltaic systems. Comparative analysis was conducted using data from Poland, the European Union, and other developed countries, allowing for the identification of both common trends and region-specific challenges. Findings: The analysis demonstrates that AI can significantly enhance the reliability and stability of PV energy supply, provided that appropriate technological, organizational, and regulatory frameworks are established. Practical implementations: The findings are illustrated by practical implementations of AI in energy forecasting, predictive maintenance, and intelligent grid management in European and Polish energy companies. Case studies highlight how AI-supported control systems enhance the stability of photovoltaic energy supply by integrating storage facilities and demand-side management. Originality/Value: This article contributes original insights into the role of AI in ensuring both energy security and operational safety of photovoltaic installations under dynamic market and regulatory conditions.

Keywords: Photovoltaics, energy security, aArtificial intelligence, machine learning; fault diagnostics, smart energy management, cybersecurity. (search for similar items in EconPapers)
JEL-codes: C63 O33 Q42 Q48 Q54 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:ers:journl:v:xxviii:y:2025:i:3:p:786-806

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