ARTIFICIAL INTELLIGENCE IN SMART GRID SYSTEMS: A COMPREHENSIVE REVIEW OF MACHINE LEARNING AND DEEP LEARNING APPLICATIONS

Abstract [English]

The integration of Artificial Intelligence (AI) into smart grid systems has revolutionized the management and optimization of electrical grids, promising significant advancements in efficiency and reliability. This comprehensive review delves into the applications of machine learning (ML) and deep learning (DL) techniques within smart grids, exploring their impact on various facets of grid management. ML algorithms have been instrumental in predictive maintenance, forecasting load and generation, and optimizing energy distribution, significantly enhancing operational efficiencies. DL models, particularly convolutional and recurrent neural networks, have been adept at handling large volumes of data from smart meters and IoT devices, facilitating real-time energy management and anomaly detection. Moreover, AI's role in integrating renewable energy sources into the grid is highlighted, addressing the challenges posed by their intermittent nature through predictive analytics that ensure a stable energy supply. AI-driven cybersecurity measures are also reviewed, underscoring their importance in protecting grid data integrity and continuity from cyber threats. The review also discusses the challenges faced in deploying AI in smart grids, including data quality, model interpretability, and the need for scalable solutions that can adapt to evolving grid architectures. Future directions for research are identified, emphasizing the development of hybrid models that combine both ML and DL approaches for enhanced performance, and the exploration of reinforcement learning for autonomous grid management. The review concludes by stressing the critical need for collaboration among researchers, industry stakeholders, and policymakers to facilitate the adoption of AI technologies that can meet future smart grid demands effectively.