Privacy and Security in Artificial Intelligence and Machine Learning Systems for Renewable Energy Big Data

This paper explores the critical intersection of security and privacy in advanced artificial intelligence (AI) and machine learning (ML) with Internet of Things (IoT) systems and edge computing applied to big data in the renewable energy (RE) sector, where the generated data is grown exponentially, presenting unique challenges in data management, analysis, and security. This study discusses the complexities of anomaly detection (AD) in RE data, examining the evolving security threats and the need for real-time processing. Through a comprehensive literature review and the proposal of an innovative framework, we address the security and privacy challenges in AD for RE data, evaluate the effectiveness of current solutions, and propose robust strategies for enhancing security measures. The study underscores the need for continuous security protocols’ adaptation to evolving threats. It emphasizes the importance of regular audits and compliance with regulatory standards to maintain the resilience of RE systems against cyber threats.