abstract

The rapid evolution of smart grids demands advanced energy information management systems that leverage Big Data and Artificial Intelligence (AI) to optimize energy distribution, enhance efficiency, and ensure grid stability. This paper explores the integration of Big Data analytics and AI-driven solutions in Smart Energy Information Management Systems (SEIMS) for next-generation smart grids. The study highlights how real-time data processing, predictive analytics, and machine learning algorithms contribute to improved energy demand forecasting, adaptive load balancing, fault detection, and automated decision-making. Through a comprehensive review of existing technologies and a quantitative analysis, this research evaluates the impact of AI-enhanced demand-side management, intelligent automation, and predictive maintenance on grid performance. Findings indicate that Big Data and AI significantly enhance smart grid efficiency by enabling self-optimizing energy networks, reducing operational costs, and improving grid resilience. The study also discusses security challenges, data privacy concerns, and implementation barriers associated with AI-driven smart grid management. The results underscore the necessity for policy frameworks, infrastructure development, and stakeholder collaboration to fully harness the potential of AI and Big Data in energy management. This research provides valuable insights for energy providers, policymakers, and technology developers in designing next-generation smart grids that are efficient, secure, and sustainable.