Artificial Intelligence (AI) has emerged as a transformative technology across various industries, and the energy sector is no exception. The increasing complexity of energy systems, driven by the proliferation of distributed generation, renewable energy sources, and dynamic demand patterns, necessitates intelligent solutions for efficient energy management. This paper presents an in-depth exploration of AI-enabled Smart Electricity Management Systems (SEMS) integrated with predictive analytics to enhance decision- making, optimize resource utilization, and ensure sustainable energy operations. This study investigates the capabilities of AI technologies, such as machine learning, neural networks, and deep learning algorithms, to forecast energy consumption, detect anomalies, and automate grid operations. Moreover, it explores predictive analytics tools that help preempt failures and improve load balancing. The combination of AI and predictive analytics leads to more responsive, adaptive, and intelligent electricity networks, supporting the goals of reliability, efficiency, and sustainability. This paper offers comprehensive insights into current applications, emerging innovations, economic and environmental impacts, regulatory implications, and future research directions of AI in electricity management.This paper delves into the transformative potential of artificial intelligence (AI) when integrated into smart electricity management systems, particularly focusing on the role of predictive analytics. These systems not only enhance energy efficiency but also support real-time decision-making, optimize load balancing, and contribute to sustainable energy consumption. The abstract outlines the key goals, methods, and anticipated impacts of implementing such intelligent systems in modern power grids.
Palanivel Rathinasabapathi Velmurugan (2025) studied this question.