Cross-Disciplinary Computational Models for Integrating Artificial Intelligence with Internet of Things and Smart Grid Technologies
Keywords:
Artificial Intelligence, Internet of Things, Smart Grid, Computational Models, Edge Computing, Cross-Disciplinary Integration, Energy Systems, Multi-Agent SystemsAbstract
The convergence of Artificial Intelligence (AI), Internet of Things (IoT), and Smart Grid (SG) technologies presents an unprecedented opportunity to modernize energy systems. However, the integration of these systems remains challenging due to heterogeneity, scalability concerns, data security, and real-time decision-making requirements. This paper explores cross-disciplinary computational models that enable seamless interaction among AI, IoT, and Smart Grid technologies. By analyzing developments, we identify key computational paradigms, review literature, and propose an integrated framework that leverages edge AI, multi-agent systems, and hybrid learning techniques. The paper also outlines a diagrammatic model for integrated architecture and presents comparative analyses of existing models. This work aims to provide a foundation for future developments in smart energy management systems that are intelligent, scalable, and sustainable.
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