The Role of Data Driven Decision Making in Smart Energy Management Systems: A Business Analytics Approach

Abstract

This study investigates how data-driven decision-making enhances Smart Energy Management Systems (SEMS) through a business analytics lens. Leveraging real-time IoT sensor data, predictive analytics, and optimization techniques, utilities can improve demand forecasting, predictive maintenance, grid optimization, and asset investment decisions. The structured framework demonstrates measurable benefits: reduced operational costs, minimized downtime, and enhanced decision accuracy. Findings underscore the impact of integrating ML‑based analytics within SEMS on operational efficiency and strategic resilience, aligning energy management with sustainability and regulatory objectives.