Smart Energy Meets Smart Security: A Comprehensive Review of AI Applications in Cybersecurity for Renewable Energy Systems

Abstract

The rapid adoption of renewable energy systems has brought forth a new set of cybersecurity challenges that require innovative solutions. In this context, artificial intelligence (AI) has emerged as a promising approach to tackle these challenges. This paper provides a comprehensive review of more than 19 studies that investigate the applications of AI in cybersecurity for renewable energy systems. By analyzing these studies, a range of opportunities and challenges associated with the integration of AI in this domain are identified. Notably, the findings indicate that over 75% of the studies acknowledge the significant potential of AI in enhancing the security of renewable energy systems. Among the various AI techniques employed, machine learning emerges as the most extensively utilized method, demonstrating an impressive detection rate of 85% and a false positive rate below 5%. However, certain challenges persist, including the limited availability of relevant data and concerns regarding the interpretability of AI models. To address these challenges, this paper concludes by providing recommendations for future research directions in this field, aiming to drive advancements in the intersection of smart energy and smart security.