AI-Enabled Big Data Analytics for Smart Energy Management

Abstract: Research questions, methods, key findings, and implications are summarized with emphasis on AI-enabled big data analytics in smart energy management. The scope, limitations, and novelty are stated, and practical and theoretical contributions are outlined. The global push for net-zero carbon emissions by 2050 necessitates the decarbonization of energy systems, but the massive deployment of renewable generation introduces intermittency and variability. Consequently, demand–supply matching has emerged as a high-priority problem. Advanced data analytics is essential for smart energy management and artificial intelligence (AI) enables intelligent decision-making by using big data analytics. However, ensuring energy data privacy and security is vital for successful adoption of AI-based solutions. Future trends, such as the emergence of the metaverse, quantum computing, and 6G networks, will further boost demand for big data analytics and AI solutions. AI-enabled big data analytics covering data acquisition pipelines, quality, governance, storage, and processing frameworks will enable various smart management paradigms: demand response, renewable generation integration, storage management, microgrid management, and fault detection.

Keywords: AI, big data analytics, smart grids, demand response, renewable integration, cyber security, data governance.