| Issue |
E3S Web Conf.
Volume 387, 2023
International Conference on Smart Engineering for Renewable Energy Technologies (ICSERET-2023)
|
|
|---|---|---|
| Article Number | 02006 | |
| Number of page(s) | 7 | |
| Section | Energy | |
| DOI | https://doi.org/10.1051/e3sconf/202338702006 | |
| Published online | 15 May 2023 | |
Optimizing Renewable Energy Management in Smart Grids Using Machine Learning
1 New Prince Shri Bhavani College Of Engineering and Technology, Approved by AICTE, Affiliated To Anna University, India
2 bannari Amman institute of Technology, Erode, India
3 Assistant Professor, Prince Dr. K. Vasudevan College of Engineering and Technology, Chennai - 127
4 Assistant Professor, Prince Shri Venkateshwara Padmavathy Engineering College, Chennai - 127
* Corresponding author: umamageswarid@bitsathy.ac.in
Renewable energy management in smart grids is a challenging problem due to the uncertainty and variability of renewable energy sources. To improve the efficiency and reliability of renewable energy utilization, various optimization techniques have been proposed. In this paper propose an approach based on the Extreme Learning Machine (ELM) algorithm with Particle Swarm Optimization (PSO) for optimizing renewable energy management in smart grids. The ELM algorithm is used to model and predict renewable energy generation, while the PSO algorithm is used to optimize the parameters of the ELM algorithm. The proposed approach is evaluated on a dataset of solar energy production and compared with other optimization techniques. The results show that the ELM-PSO approach can improve the accuracy of renewable energy predictions and reduce energy costs in smart grids. The proposed approach can be used in various renewable energy systems, such as wind turbines, solar panels, and hydroelectric power plants, to improve the efficiency and reliability of renewable energy utilization.
Key words: Renewable energy management / Smart grid / Optimization / Extreme Machine Learning / Particle Swarm Optimization
© The Authors, published by EDP Sciences, 2023
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