Optimizing Renewable Energy Management in Smart Grids Using Machine Learning
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 bas...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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EDP Sciences
2023-01-01
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| Series: | E3S Web of Conferences |
| Subjects: | |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/24/e3sconf_icseret2023_02006.pdf |
| _version_ | 1827930124095324160 |
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| author | G.B. Santhi Maheswari Duma M. Anitha Priyadharshini R. Indira |
| author_facet | G.B. Santhi Maheswari Duma M. Anitha Priyadharshini R. Indira |
| author_sort | G.B. Santhi |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-13T06:32:07Z |
| format | Article |
| id | doaj.art-9a2086be7c1a40d19fbc082d31cfc2f4 |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-03-13T06:32:07Z |
| publishDate | 2023-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-9a2086be7c1a40d19fbc082d31cfc2f42023-06-09T09:06:53ZengEDP SciencesE3S Web of Conferences2267-12422023-01-013870200610.1051/e3sconf/202338702006e3sconf_icseret2023_02006Optimizing Renewable Energy Management in Smart Grids Using Machine LearningG.B. Santhi0Maheswari Duma1M. Anitha2Priyadharshini R. Indira3New Prince Shri Bhavani College Of Engineering and Technology, Approved by AICTE, Affiliated To Anna Universitybannari Amman institute of TechnologyAssistant Professor, Prince Dr. K. Vasudevan College of Engineering and TechnologyAssistant Professor, Prince Shri Venkateshwara Padmavathy Engineering CollegeRenewable 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.https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/24/e3sconf_icseret2023_02006.pdfrenewable energy managementsmart gridoptimizationextreme machine learningparticle swarm optimization |
| spellingShingle | G.B. Santhi Maheswari Duma M. Anitha Priyadharshini R. Indira Optimizing Renewable Energy Management in Smart Grids Using Machine Learning E3S Web of Conferences renewable energy management smart grid optimization extreme machine learning particle swarm optimization |
| title | Optimizing Renewable Energy Management in Smart Grids Using Machine Learning |
| title_full | Optimizing Renewable Energy Management in Smart Grids Using Machine Learning |
| title_fullStr | Optimizing Renewable Energy Management in Smart Grids Using Machine Learning |
| title_full_unstemmed | Optimizing Renewable Energy Management in Smart Grids Using Machine Learning |
| title_short | Optimizing Renewable Energy Management in Smart Grids Using Machine Learning |
| title_sort | optimizing renewable energy management in smart grids using machine learning |
| topic | renewable energy management smart grid optimization extreme machine learning particle swarm optimization |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/24/e3sconf_icseret2023_02006.pdf |
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