The Impact of AI-Based Predictive Maintenance and Demand Response Integration on Smart Grid Operational Efficiency: The Moderating Role of Cybersecurity Readiness

This study examines the influence of demand response integration and AI-based predictive maintenance on smart grid operational efficiency, with an emphasis on the moderating effect of cybersecurity readiness. Based on the theories of contingency and socio-technical systems, the study views smart grid efficiency as a result of both institutional readiness and technological innovation. Using a 5-point Likert scale, a quantitative survey was given to 385 professionals working in the cybersecurity and energy industries in Vietnam. The results show that demand response integration (β = 0.508) and AI-based predictive maintenance (β = 0.635) both considerably improve operational efficiency. Additionally, the association between AI-based maintenance and efficiency was shown to be moderated by cybersecurity preparedness (β = 0.472), indicating that digital protection procedures are necessary to benefit from predictive technology fully. By experimentally connecting cybersecurity with smart grid functioning, the study closes a significant gap in the literature and offers a comprehensive paradigm for resilient energy modernization. The findings provide useful information for engineers, legislators, and energy companies attempting to strike a balance between risk reduction and innovation in digitalized energy ecosystems.