Data Analytics for Enhanced Business Intelligence in Energy-Saving Distributed Systems

This research examines how data analytics might improve Business Intelligence (BI) in energy-saving distributed systems to improve energy management and sustainability. Secondary data-based reviews synthesize literature on data analytics frameworks, data processing methods, and BI tactics in distributed energy scenarios. According to critical results, descriptive, diagnostic, predictive, and prescriptive analytics turn raw data into energy-efficient insights. Descriptive and diagnostic analytics highlight historical trends and inefficiencies, whereas predictive and prescriptive methods maximize resource allocation and real-time decision-making. Adaptive energy management requires robust BI frameworks with centralized data warehousing, visualization, and real-time analytics. However, enormous data volume, real-time processing limits, data security, and lack of standards limit these analytics' usefulness. Policy guidelines should include cybersecurity safeguards, AI and edge computing integration incentives, and standardized protocols to improve data processing and system interoperability. These findings demonstrate the importance of data-driven BI in improving energy efficiency and sustainability in distributed energy systems and meeting global energy targets.