RETRACTED: Machine learning based load prediction in smart‐grid under different contract scenario
Abstract Many progressed information scientific strategies, particularly Artificial Intelligence (AI) and profound learning methods, have been proposed and tracked down wide applications in our general public. This proposition creates information driven arrangements by utilizing the most recent prof...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-04-01
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| Series: | IET Generation, Transmission & Distribution |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/gtd2.12828 |
| _version_ | 1826968673924939776 |
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| author | Piyush Kumar Yadav Rajnish Bhasker Albert Alexander Stonier Geno Peter Arun Vijayakumar Vivekananda Ganji |
| author_facet | Piyush Kumar Yadav Rajnish Bhasker Albert Alexander Stonier Geno Peter Arun Vijayakumar Vivekananda Ganji |
| author_sort | Piyush Kumar Yadav |
| collection | DOAJ |
| description | Abstract Many progressed information scientific strategies, particularly Artificial Intelligence (AI) and profound learning methods, have been proposed and tracked down wide applications in our general public. This proposition creates information driven arrangements by utilizing the most recent profound learning and AI innovation, including outfit learning, meta‐learning and move learning, for energy the executives framework issues. Genuine world datasets are tried on proposed models contrasted and best in class plans, which exhibit the predominant presentation of the proposed model. In this proposition, the engineering of the Smart Grid testbed is additionally planned and created by using ML calculations and true remote correspondence frameworks to such an extent that constant plan necessities of Smart Grid testbed is met by this reconfigurable system with stacking of full convention in medium access control (MAC) and physical layers (PHY). The proposed engineering has the reconfiguration property in view of the organization of remote correspondence and trend setting innovations of Information and communication technologies (ICT) which incorporates Artificial Intelligence (AI) calculation. The fundamental plan objectives of the Smart Grid testbed is to make it simple to construct, reconfigure and scale to address the framework level prerequisites and to address the ongoing necessities. |
| first_indexed | 2024-04-09T16:47:31Z |
| format | Article |
| id | doaj.art-ff351f42c0654da68e975d91d9e67128 |
| institution | Directory Open Access Journal |
| issn | 1751-8687 1751-8695 |
| language | English |
| last_indexed | 2025-02-18T04:15:37Z |
| publishDate | 2023-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Generation, Transmission & Distribution |
| spelling | doaj.art-ff351f42c0654da68e975d91d9e671282024-11-20T10:45:46ZengWileyIET Generation, Transmission & Distribution1751-86871751-86952023-04-011781918193110.1049/gtd2.12828RETRACTED: Machine learning based load prediction in smart‐grid under different contract scenarioPiyush Kumar Yadav0Rajnish Bhasker1Albert Alexander Stonier2Geno Peter3Arun Vijayakumar4Vivekananda Ganji5Department of Electrical Engineering Veer Bahadur Singh Purvanchal University Jaunpur IndiaDepartment of Electrical Engineering Veer Bahadur Singh Purvanchal University Jaunpur IndiaSchool of Electrical Engineering Vellore Institute of Technology Vellore IndiaCRISD University of Technology Sarawak Sibu MalaysiaDepartment of Electrical and Electronics Engineering Sree Vidyanikethan Engineering College Tirupati IndiaDepartment of Electrical and Computer Engineering Debre Tabor University Amhara EthiopiaAbstract Many progressed information scientific strategies, particularly Artificial Intelligence (AI) and profound learning methods, have been proposed and tracked down wide applications in our general public. This proposition creates information driven arrangements by utilizing the most recent profound learning and AI innovation, including outfit learning, meta‐learning and move learning, for energy the executives framework issues. Genuine world datasets are tried on proposed models contrasted and best in class plans, which exhibit the predominant presentation of the proposed model. In this proposition, the engineering of the Smart Grid testbed is additionally planned and created by using ML calculations and true remote correspondence frameworks to such an extent that constant plan necessities of Smart Grid testbed is met by this reconfigurable system with stacking of full convention in medium access control (MAC) and physical layers (PHY). The proposed engineering has the reconfiguration property in view of the organization of remote correspondence and trend setting innovations of Information and communication technologies (ICT) which incorporates Artificial Intelligence (AI) calculation. The fundamental plan objectives of the Smart Grid testbed is to make it simple to construct, reconfigure and scale to address the framework level prerequisites and to address the ongoing necessities.https://doi.org/10.1049/gtd2.12828artificial intelligenceload forecastingmachine learningmicro‐gridsmart grid |
| spellingShingle | Piyush Kumar Yadav Rajnish Bhasker Albert Alexander Stonier Geno Peter Arun Vijayakumar Vivekananda Ganji RETRACTED: Machine learning based load prediction in smart‐grid under different contract scenario IET Generation, Transmission & Distribution artificial intelligence load forecasting machine learning micro‐grid smart grid |
| title | RETRACTED: Machine learning based load prediction in smart‐grid under different contract scenario |
| title_full | RETRACTED: Machine learning based load prediction in smart‐grid under different contract scenario |
| title_fullStr | RETRACTED: Machine learning based load prediction in smart‐grid under different contract scenario |
| title_full_unstemmed | RETRACTED: Machine learning based load prediction in smart‐grid under different contract scenario |
| title_short | RETRACTED: Machine learning based load prediction in smart‐grid under different contract scenario |
| title_sort | retracted machine learning based load prediction in smart grid under different contract scenario |
| topic | artificial intelligence load forecasting machine learning micro‐grid smart grid |
| url | https://doi.org/10.1049/gtd2.12828 |
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