Abstract :
This article explores the transformative potential of machine learning (ML) in enhancing the efficiency and management of smart grids. As electrical grids become increasingly complex and demand for energy grows, traditional management methods are proving insufficient. Smart grids, enhanced with machine learning, offer a dynamic solution capable of handling real-time data analysis, predictive maintenance, and efficient energy distribution. This in-depth review covers the essential components of smart grids, various machine learning techniques applicable in this context, and specific applications such as optimizing demand response and integrating renewable energy sources. Through a series of case studies, the article illustrates the practical benefits and challenges of implementing ML in smart grids, providing a nuanced understanding of current successes and areas for improvement. Additionally, it discusses emerging trends and the future of smart grids as machine learning technologies continue to evolve. This comprehensive analysis aims to highlight how machine learning not only enhances grid management but also drives the innovation necessary for future sustainability and efficiency improvements.
Keyword :
Smart Grids, Machine Learning, Grid Management, Energy Efficiency, Predictive Maintenance, Demand Forecasting, Anomaly Detection, Renewable Energy Integration.