AI and machine learning for secure data exchange in decentralized energy markets on the cloud

Abstract

The increasing digitization of energy systems and the advent of decentralized energy markets have introduced significant challenges in ensuring secure, efficient, and scalable data exchange, particularly within cloud-based infrastructures. This research explores the integration of artificial intelligence (AI) and machine learning (ML) techniques to enhance the security and performance of data exchange mechanisms in decentralized energy markets operating on the cloud. By leveraging advanced AI-driven anomaly detection, federated learning frameworks, and blockchain-based trust protocols, this study aims to mitigate threats related to data breaches, unauthorized access, and information asymmetry among market participants. The paper presents a comprehensive analysis of machine learning algorithms tailored for secure data transmission, real-time threat detection, and adaptive encryption strategies, with a focus on preserving data integrity, confidentiality, and system resilience. Case studies and simulation results underscore the applicability of proposed solutions in real-world distributed energy environments. This work contributes to advancing secure, intelligent, and sustainable data exchange architectures for future energy systems.