Issue |
E3S Web Conf.
Volume 387, 2023
International Conference on Smart Engineering for Renewable Energy Technologies (ICSERET-2023)
|
|
---|---|---|
Article Number | 02004 | |
Number of page(s) | 7 | |
Section | Energy | |
DOI | https://doi.org/10.1051/e3sconf/202338702004 | |
Published online | 15 May 2023 |
Integrating Renewable Energy Sources with Micro Grid Using IOT and Machine Learning
1 Bannari Amman Institute of Technology, Erode, India
2 New Prince Shri Bhavani College Of Engineering and Technology, Approved by AICTE, Affiliated To Anna University
3 Assistant Professor, Prince Shri Venkateshwara Padmavathy Engineering College, Chennai - 127
4 Assistant Professor, Prince Dr. K. Vasudevan College of Engineering and Technology, Chennai - 127
* Corresponding author: preetha@bitsathy.ac.in
The integration of renewable energy sources with microgrids using IoT and energy management technologies has become a promising solution for achieving sustainable and efficient energy systems. In this paper, propose a methodology for integrating renewable energy sources with microgrids using IoT and energy management technologies, and apply an Artificial Neural Network (ANN) algorithm for energy demand prediction. The proposed methodology aims to optimize the energy consumption of the micro grid by utilizing renewable energy sources and energy storage devices. Validate the proposed methodology using a real-world dataset, and compare the performance with traditional forecasting methods. The results show that the proposed methodology outperforms traditional methods in terms of accuracy and efficiency. The proposed methodology can be utilized in various micro grid applications for load forecasting and energy consumption optimization.
Key words: Microgrid / Renewable Energy / IoT / Energy Management / Artificial Neural Network / Energy Demand Prediction
© The Authors, published by EDP Sciences, 2023
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