Exploring the Potential of Artificial Intelligence to Enhance Energy Efficiency in Smart Grid Systems: A Detailed Review and Future Directions

Abstract

The rapid evolution of energy infrastructure, driven by increasing energy demands and the integration of renewable energy sources, necessitates the transformation of traditional power grids into smart grid systems. These smart grids leverage advanced technologies to enhance energy efficiency, reliability, and sustainability. Artificial Intelligence (AI) emerges as a pivotal technology in this transformation, offering sophisticated tools and techniques to optimize various aspects of smart grid operations. This paper presents a comprehensive review of the potential of AI to enhance energy efficiency in smart grid systems. It examines AI applications in demand forecasting, energy management, fault detection, and load balancing. By analyzing the current state of AI integration in smart grids and exploring future directions, the study aims to highlight how AI can contribute to more efficient, reliable, and sustainable energy systems. The review encompasses various AI methodologies, including machine learning, deep learning, reinforcement learning, and expert systems, assessing their effectiveness in different smart grid applications. The paper also discusses the challenges and opportunities associated with AI deployment in smart grids, providing insights into how AI can be harnessed to address emerging energy challenges and support the development of next-generation energy systems.