Deep Learning in Smart Grid Technology: A Review of Recent Advancements and Future Prospects
<p>The current electric power system witnesses a significant transition into Smart Grids (SG) as a promising landscape for high grid reliability and efficient energy management. This ongoing transition undergoes rapid changes, requiring a plethora of advanced methodologies to process the big d...
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| مؤلفون آخرون: | , , , |
| منشور في: |
2021
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| _version_ | 1864513562718765056 |
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| author | Mohamed Massaoudi (16888710) |
| author2 | Haitham Abu-Rub (16855500) Shady S. Refaat (16864269) Ines Chihi (16888713) Fakhreddine S. Oueslati (16888716) |
| author2_role | author author author author |
| author_facet | Mohamed Massaoudi (16888710) Haitham Abu-Rub (16855500) Shady S. Refaat (16864269) Ines Chihi (16888713) Fakhreddine S. Oueslati (16888716) |
| author_role | author |
| dc.creator.none.fl_str_mv | Mohamed Massaoudi (16888710) Haitham Abu-Rub (16855500) Shady S. Refaat (16864269) Ines Chihi (16888713) Fakhreddine S. Oueslati (16888716) |
| dc.date.none.fl_str_mv | 2021-04-05T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2021.3071269 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Deep_Learning_in_Smart_Grid_Technology_A_Review_of_Recent_Advancements_and_Future_Prospects/24049236 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Electrical engineering Information and computing sciences Distributed computing and systems software Machine learning Forecasting Deep learning Artificial intelligence Smart grids Collaborative work Predictive models Renewable energy sources Deep neural networks Edge computing Distributed and federated learning Power systems |
| dc.title.none.fl_str_mv | Deep Learning in Smart Grid Technology: A Review of Recent Advancements and Future Prospects |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>The current electric power system witnesses a significant transition into Smart Grids (SG) as a promising landscape for high grid reliability and efficient energy management. This ongoing transition undergoes rapid changes, requiring a plethora of advanced methodologies to process the big data generated by various units. In this context, SG stands tied very closely to Deep Learning (DL) as an emerging technology for creating a more decentralized and intelligent energy paradigm while integrating high intelligence in supervisory and operational decision-making. Motivated by the outstanding success of DL-based prediction methods, this article attempts to provide a thorough review from a broad perspective on the state-of-the-art advances of DL in SG systems. Firstly, a bibliometric analysis has been conducted to categorize this review's methodology. Further, we taxonomically delve into the mechanism behind some of the trending DL algorithms. We then showcase the DL enabling technologies in SG, such as federated learning, edge intelligence, and distributed computing. Finally, challenges and research frontiers are provided to serve as guidelines for future work in the futuristic power grid domain. This study's core objective is to foster the synergy between these two fields for decision-makers and researchers to accelerate DL's practical deployment for SG systems.</p><h2>Other Information</h2><p>Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2021.3071269" target="_blank">https://dx.doi.org/10.1109/access.2021.3071269</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_88102e6200d5569ca9f8c37b91cfb367 |
| identifier_str_mv | 10.1109/access.2021.3071269 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24049236 |
| publishDate | 2021 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Deep Learning in Smart Grid Technology: A Review of Recent Advancements and Future ProspectsMohamed Massaoudi (16888710)Haitham Abu-Rub (16855500)Shady S. Refaat (16864269)Ines Chihi (16888713)Fakhreddine S. Oueslati (16888716)EngineeringElectrical engineeringInformation and computing sciencesDistributed computing and systems softwareMachine learningForecastingDeep learningArtificial intelligenceSmart gridsCollaborative workPredictive modelsRenewable energy sourcesDeep neural networksEdge computingDistributed and federated learningPower systems<p>The current electric power system witnesses a significant transition into Smart Grids (SG) as a promising landscape for high grid reliability and efficient energy management. This ongoing transition undergoes rapid changes, requiring a plethora of advanced methodologies to process the big data generated by various units. In this context, SG stands tied very closely to Deep Learning (DL) as an emerging technology for creating a more decentralized and intelligent energy paradigm while integrating high intelligence in supervisory and operational decision-making. Motivated by the outstanding success of DL-based prediction methods, this article attempts to provide a thorough review from a broad perspective on the state-of-the-art advances of DL in SG systems. Firstly, a bibliometric analysis has been conducted to categorize this review's methodology. Further, we taxonomically delve into the mechanism behind some of the trending DL algorithms. We then showcase the DL enabling technologies in SG, such as federated learning, edge intelligence, and distributed computing. Finally, challenges and research frontiers are provided to serve as guidelines for future work in the futuristic power grid domain. This study's core objective is to foster the synergy between these two fields for decision-makers and researchers to accelerate DL's practical deployment for SG systems.</p><h2>Other Information</h2><p>Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2021.3071269" target="_blank">https://dx.doi.org/10.1109/access.2021.3071269</a></p>2021-04-05T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2021.3071269https://figshare.com/articles/journal_contribution/Deep_Learning_in_Smart_Grid_Technology_A_Review_of_Recent_Advancements_and_Future_Prospects/24049236CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/240492362021-04-05T00:00:00Z |
| spellingShingle | Deep Learning in Smart Grid Technology: A Review of Recent Advancements and Future Prospects Mohamed Massaoudi (16888710) Engineering Electrical engineering Information and computing sciences Distributed computing and systems software Machine learning Forecasting Deep learning Artificial intelligence Smart grids Collaborative work Predictive models Renewable energy sources Deep neural networks Edge computing Distributed and federated learning Power systems |
| status_str | publishedVersion |
| title | Deep Learning in Smart Grid Technology: A Review of Recent Advancements and Future Prospects |
| title_full | Deep Learning in Smart Grid Technology: A Review of Recent Advancements and Future Prospects |
| title_fullStr | Deep Learning in Smart Grid Technology: A Review of Recent Advancements and Future Prospects |
| title_full_unstemmed | Deep Learning in Smart Grid Technology: A Review of Recent Advancements and Future Prospects |
| title_short | Deep Learning in Smart Grid Technology: A Review of Recent Advancements and Future Prospects |
| title_sort | Deep Learning in Smart Grid Technology: A Review of Recent Advancements and Future Prospects |
| topic | Engineering Electrical engineering Information and computing sciences Distributed computing and systems software Machine learning Forecasting Deep learning Artificial intelligence Smart grids Collaborative work Predictive models Renewable energy sources Deep neural networks Edge computing Distributed and federated learning Power systems |