A Survey on Machine Learning Techniques in Smart Grids Based on Wireless Sensor Networks
Smart grids (SGs) are crucial to the efficiency and sustainability of modern energy systems. As the world’s population continues to increase, so does the need for energy, and traditional energy systems are struggling to keep up. In this context, this study reviews the possibilities of dep...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10818473/ |
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| author | Ashraf M. Etman Mohamed S. Abdalzaher Ahmed A. Emran Ahmed Yahya Mostafa Shaaban |
| author_facet | Ashraf M. Etman Mohamed S. Abdalzaher Ahmed A. Emran Ahmed Yahya Mostafa Shaaban |
| author_sort | Ashraf M. Etman |
| collection | DOAJ |
| description | Smart grids (SGs) are crucial to the efficiency and sustainability of modern energy systems. As the world’s population continues to increase, so does the need for energy, and traditional energy systems are struggling to keep up. In this context, this study reviews the possibilities of deploying machine learning (ML) on wireless sensor networks (WSNs) in smart grid systems. In several ways, SGs may gain from combining WSNs with ML, including enhance system reliability, sustainability, improve fault detection, and increase energy efficiency. This paper offers an extensive review of pertinent research emphasizing the use of supervised, unsupervised, and reinforcement learning approaches. The evaluation contains 234 peer reviewed articles from highly regarded academic journals and conferences covering the years 2017 through 2024 which depict the effectiveness of supervised techniques on WSNs in the field of SGs. In addition the paper presents set of the most usable datasets in the field of WSNs and SGs, and introduces a comparison between our paper and relevant surveys. The study also analyses the opportunities and challenges related to the application of WSNs and ML in SGs and offers possible research directions. Overall, the study makes it clear that combining WSNs with ML may significantly contribute to the creation of smart grid systems that are more effective, dependable, and sustainable. |
| format | Article |
| id | doaj-art-65c03ad4d9624829a3a109334fead650 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-65c03ad4d9624829a3a109334fead6502025-01-10T00:02:11ZengIEEEIEEE Access2169-35362025-01-01132604262710.1109/ACCESS.2024.352409710818473A Survey on Machine Learning Techniques in Smart Grids Based on Wireless Sensor NetworksAshraf M. Etman0Mohamed S. Abdalzaher1https://orcid.org/0000-0002-9197-0306Ahmed A. Emran2https://orcid.org/0000-0002-8912-2236Ahmed Yahya3Mostafa Shaaban4https://orcid.org/0000-0001-5134-0601Department of Electrical Engineering, Modern Academy For Engineering and Technology, Cairo, EgyptDepartment of Electrical Engineering, American University of Sharjah, Sharjah, United Arab EmiratesDepartment of Electrical Engineering, Al-Azhar University, Nasr City, EgyptDepartment of Electrical Engineering, Al-Azhar University, Nasr City, EgyptDepartment of Electrical Engineering, American University of Sharjah, Sharjah, United Arab EmiratesSmart grids (SGs) are crucial to the efficiency and sustainability of modern energy systems. As the world’s population continues to increase, so does the need for energy, and traditional energy systems are struggling to keep up. In this context, this study reviews the possibilities of deploying machine learning (ML) on wireless sensor networks (WSNs) in smart grid systems. In several ways, SGs may gain from combining WSNs with ML, including enhance system reliability, sustainability, improve fault detection, and increase energy efficiency. This paper offers an extensive review of pertinent research emphasizing the use of supervised, unsupervised, and reinforcement learning approaches. The evaluation contains 234 peer reviewed articles from highly regarded academic journals and conferences covering the years 2017 through 2024 which depict the effectiveness of supervised techniques on WSNs in the field of SGs. In addition the paper presents set of the most usable datasets in the field of WSNs and SGs, and introduces a comparison between our paper and relevant surveys. The study also analyses the opportunities and challenges related to the application of WSNs and ML in SGs and offers possible research directions. Overall, the study makes it clear that combining WSNs with ML may significantly contribute to the creation of smart grid systems that are more effective, dependable, and sustainable.https://ieeexplore.ieee.org/document/10818473/Wireless sensor networkssmart gridsmachine learningsupervised MLsmart sensors |
| spellingShingle | Ashraf M. Etman Mohamed S. Abdalzaher Ahmed A. Emran Ahmed Yahya Mostafa Shaaban A Survey on Machine Learning Techniques in Smart Grids Based on Wireless Sensor Networks IEEE Access Wireless sensor networks smart grids machine learning supervised ML smart sensors |
| title | A Survey on Machine Learning Techniques in Smart Grids Based on Wireless Sensor Networks |
| title_full | A Survey on Machine Learning Techniques in Smart Grids Based on Wireless Sensor Networks |
| title_fullStr | A Survey on Machine Learning Techniques in Smart Grids Based on Wireless Sensor Networks |
| title_full_unstemmed | A Survey on Machine Learning Techniques in Smart Grids Based on Wireless Sensor Networks |
| title_short | A Survey on Machine Learning Techniques in Smart Grids Based on Wireless Sensor Networks |
| title_sort | survey on machine learning techniques in smart grids based on wireless sensor networks |
| topic | Wireless sensor networks smart grids machine learning supervised ML smart sensors |
| url | https://ieeexplore.ieee.org/document/10818473/ |
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