Abstract
The concept of Smart Grids (SG) has emerged as a solution to address challenges in traditional power systems, including resource inefficiency, reliability issues, and instability. Since its inception in the early 21st century, Smart Grid technology has undergone significant development, integrating advanced information communication and automation technologies with conventional power infrastructure. This integration enhances efficiency, reliability, and sustainability, while enabling the integration of renewable energy sources and optimizing energy distribution and consumption. Machine learning algorithms play a pivotal role in the development of Smart Grids, facilitating energy consumption prediction, optimization, anomaly detection, and fault diagnosis. This paper explores methodologies for developing and improving machine learning algorithms for efficient energy consumption prediction and management within Smart Grids. It discusses the application of deep learning techniques, reinforcement learning, and integration with the Internet of Things (IoT) to enhance energy management systems. The study highlights the potential impact of deep convolutional neural networks (CNNs) on energy consumption regulation and emphasizes the need for further research to address challenges associated with model complexity and data requirements in Smart Grid contexts.
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