Power Theft Detection in Smart Grids Using Quantum Machine Learning
Electricity theft can lead to enormous economic losses and cause operational and security problems for electricity networks and utilities. Most current research has focused on electricity theft detection in the consumption sector. However, the high penetration rate of distributed generation (DG) can...
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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/10949078/ |
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| author | Konstantinos Blazakis Nikolaos Schetakis Mahmoud M. Badr Davit Aghamalyan Konstantinos Stavrakakis Georgios Stavrakakis |
| author_facet | Konstantinos Blazakis Nikolaos Schetakis Mahmoud M. Badr Davit Aghamalyan Konstantinos Stavrakakis Georgios Stavrakakis |
| author_sort | Konstantinos Blazakis |
| collection | DOAJ |
| description | Electricity theft can lead to enormous economic losses and cause operational and security problems for electricity networks and utilities. Most current research has focused on electricity theft detection in the consumption sector. However, the high penetration rate of distributed generation (DG) can lead to an increase in power theft attacks in this sector via smart meter manipulation. This study is an extension of prior works focused on electricity theft detection in the consumption and generation domains of a smart grid environment with DG. This study proposes a novel electricity theft detection framework based on quantum machine learning (QML). The elegant field of QML has been used to demonstrate that data classification becomes more efficient in higher-dimensional spaces. An extensive numerical study was conducted to determine the type of QML architecture that can perform well and efficiently in electricity theft detection cases. The technique presented here has not yet been extensively studied in the domain of energy theft detection. Extensive experiments were conducted to assess this approach, and an accuracy of 0.87 was achieved with respect to the classical consumption domain, whereas an accuracy of 0.977 was achieved with respect to the net metering domain. |
| format | Article |
| id | doaj-art-8f3afc1902a04f9f9136d3d210c72ff8 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-8f3afc1902a04f9f9136d3d210c72ff82025-08-20T02:16:33ZengIEEEIEEE Access2169-35362025-01-0113615116152510.1109/ACCESS.2025.355814310949078Power Theft Detection in Smart Grids Using Quantum Machine LearningKonstantinos Blazakis0https://orcid.org/0000-0001-8410-1818Nikolaos Schetakis1https://orcid.org/0000-0002-8893-219XMahmoud M. Badr2https://orcid.org/0000-0002-8986-001XDavit Aghamalyan3Konstantinos Stavrakakis4Georgios Stavrakakis5Department of Electrical and Computer Engineering, Hellenic Mediterranean University, Heraklion, GreeceSchool of Production Engineering and Management, Technical University of Crete, Chania, GreeceDepartment of Network and Computer Security, College of Engineering, SUNY Polytechnic Institute, Utica, NY, USAInstitute of High Performance Computing, Agency for Science, Technology, and Research (A*STAR), Fusionopolis, SingaporeDepartment of Quantum and Computer Engineering, Delft University of Technology, Delft, The NetherlandsSchool of Electrical and Computer Engineering, Technical University of Crete, Chania, GreeceElectricity theft can lead to enormous economic losses and cause operational and security problems for electricity networks and utilities. Most current research has focused on electricity theft detection in the consumption sector. However, the high penetration rate of distributed generation (DG) can lead to an increase in power theft attacks in this sector via smart meter manipulation. This study is an extension of prior works focused on electricity theft detection in the consumption and generation domains of a smart grid environment with DG. This study proposes a novel electricity theft detection framework based on quantum machine learning (QML). The elegant field of QML has been used to demonstrate that data classification becomes more efficient in higher-dimensional spaces. An extensive numerical study was conducted to determine the type of QML architecture that can perform well and efficiently in electricity theft detection cases. The technique presented here has not yet been extensively studied in the domain of energy theft detection. Extensive experiments were conducted to assess this approach, and an accuracy of 0.87 was achieved with respect to the classical consumption domain, whereas an accuracy of 0.977 was achieved with respect to the net metering domain.https://ieeexplore.ieee.org/document/10949078/Distributed generationnet meteringphotovoltaic (PV) electric energypower theft detectionquantum machine learningsmart grid |
| spellingShingle | Konstantinos Blazakis Nikolaos Schetakis Mahmoud M. Badr Davit Aghamalyan Konstantinos Stavrakakis Georgios Stavrakakis Power Theft Detection in Smart Grids Using Quantum Machine Learning IEEE Access Distributed generation net metering photovoltaic (PV) electric energy power theft detection quantum machine learning smart grid |
| title | Power Theft Detection in Smart Grids Using Quantum Machine Learning |
| title_full | Power Theft Detection in Smart Grids Using Quantum Machine Learning |
| title_fullStr | Power Theft Detection in Smart Grids Using Quantum Machine Learning |
| title_full_unstemmed | Power Theft Detection in Smart Grids Using Quantum Machine Learning |
| title_short | Power Theft Detection in Smart Grids Using Quantum Machine Learning |
| title_sort | power theft detection in smart grids using quantum machine learning |
| topic | Distributed generation net metering photovoltaic (PV) electric energy power theft detection quantum machine learning smart grid |
| url | https://ieeexplore.ieee.org/document/10949078/ |
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