Issue |
E3S Web of Conf.
Volume 540, 2024
1st International Conference on Power and Energy Systems (ICPES 2023)
|
|
---|---|---|
Article Number | 10036 | |
Number of page(s) | 10 | |
Section | Grid Connected Systems | |
DOI | https://doi.org/10.1051/e3sconf/202454010036 | |
Published online | 21 June 2024 |
Integrating Renewable Energy Sources with Micro Grid Using IOT and Machine Learning
* Assistant Professor, School of Commerce, Mount Carmel College, Autonomous, Bangalore
† Uttaranchal Institute of Technology, Uttaranchal University, Uttarakhand, India
‡ Assistant Professor, Department of CSE, Prince Shri Venkateshwara Padmavathy Engineering College, Chennai - 127
§ Department of Computer Science &Engineering, IES University, IES College of Technology, Madhya Pradesh 462044 India, Bhopal .
** The Islamic university, Najaf, Iraq
6 Associate Professor, Department of Computer Engineering, Genba Sopanrao Moze College of Engineering, Balewadi, India Email: ratnaraj.jambi@gmail.com, Pune, Maharashtra
* Corresponding Author :Vedapradha.r@gmail.com
† shivanijoshi2409@gmail.com
‡ s.bhuvaneswari_cse@psvpec.in
§ research@iesbpl.ac.in
** abathermahmood560@gmail.com
The power system landscape has undergone significant changes over the past few decades, marked by increasing electricity demand, power losses, grid failures, and the absence of smart technology. Simultaneously, security threats have escalated, and conventional power grids are struggling to cope with these challenges. In response to these evolving demands, the Internet of Things (IoT) has rapidly gained prominence due to its transformative potential. By incorporating IoT technology into power grids, we have the ability to improve the efficiency, sustainability, scalability, capacity, reliability, and stability of traditional grid systems. 1. This document provides an in-depth analysis of IoT-enabled intelligent power grids, emphasizing the importance of tackling security concerns, examining diverse use cases, and deliberating on alternative frameworks. Furthermore, we explore IoT and non-IoT technologies utilized in smart grid systems, such as sensing, computing technologies, communication, and applicable standards.
Key words: Energy Management / Artificial Neural Network / Microgrid / Renewable Energy / IoT / Energy Demand Prediction
© 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.
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