Issue |
E3S Web of Conf.
Volume 524, 2024
VII International Conference on Actual Problems of the Energy Complex and Environmental Protection (APEC-VII-2024)
|
|
---|---|---|
Article Number | 01003 | |
Number of page(s) | 7 | |
Section | Issues of the Energy Complex | |
DOI | https://doi.org/10.1051/e3sconf/202452401003 | |
Published online | 16 May 2024 |
Advancing energy efficiency: Harnessing machine learning for smart grid management
Turkmen state institute of economics and management, Ashgabat, Turkmenistan
* Corresponding author: narly233@gmail.com
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.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.