Systematic Review of Real-Time Data Analytics in the Energy Sector: Advancing Efficiency and Sustainability

Abstract

This study systematically reviews the role of real-time analytics in enhancing efficiency and sustainability in the energy sector, focusing on its applications, challenges, and future opportunities. Leveraging advanced technologies like IoT, big data, AI, and machine learning, real-time analytics enables instantaneous data processing and actionable insights, transforming energy management. Key applications include predictive maintenance, dynamic grid optimization, and renewable energy integration, which collectively enhance operational efficiency and reduce energy waste. For example, real-time monitoring reduces transmission losses, balances energy supply and demand, and ensures the stability of renewable systems like solar and wind energy. Real-time analytics also supports sustainability goals by minimizing carbon emissions, optimizing fossil fuel efficiency, and empowering consumers through smart meters to adopt energy-efficient behaviors. However, the review identifies significant challenges, including high implementation costs, data security risks, and a lack of skilled professionals, which hinder widespread adoption, particularly in developing regions. The review emphasizes the need for investments, workforce training, and secure data systems to address these barriers. Emerging technologies such as blockchain, edge AI, and quantum computing present promising opportunities to overcome current limitations. Blockchain facilitates secure, decentralized energy transactions, while edge AI enhances scalability and reduces latency in distributed systems. Quantum computing, with its unparalleled processing power, could revolutionize energy.