Electricity theft detection in smart grid using machine learning
Nowadays, electricity theft is a major issue in many countries and poses a significant financial loss for global power utilities. Conventional Electricity Theft Detection (ETD) models face challenges such as the curse of dimensionality and highly imbalanced electricity consumption data distribution....
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-03-01
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| Series: | Frontiers in Energy Research |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2024.1383090/full |
| _version_ | 1797256463763111936 |
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| author | Hasnain Iftikhar Hasnain Iftikhar Nitasha Khan Muhammad Amir Raza Ghulam Abbas Murad Khan Mouloud Aoudia Ezzeddine Touti Ezzeddine Touti Ahmed Emara Ahmed Emara |
| author_facet | Hasnain Iftikhar Hasnain Iftikhar Nitasha Khan Muhammad Amir Raza Ghulam Abbas Murad Khan Mouloud Aoudia Ezzeddine Touti Ezzeddine Touti Ahmed Emara Ahmed Emara |
| author_sort | Hasnain Iftikhar |
| collection | DOAJ |
| description | Nowadays, electricity theft is a major issue in many countries and poses a significant financial loss for global power utilities. Conventional Electricity Theft Detection (ETD) models face challenges such as the curse of dimensionality and highly imbalanced electricity consumption data distribution. To overcome these problems, a hybrid system Multi-Layer Perceptron (MLP) approach with Gated Recurrent Units (GRU) is proposed in this work. The proposed hybrid system is applied to analyze and solve electricity theft using data from the Chinese National Grid Corporation (CNGC). In the proposed hybrid system, first, preprocess the data; second, balance the data using the k-means Synthetic Minority Oversampling Technique (SMOTE) technique; third, apply the GTU model to the extracted purified data; fourth, apply the MLP model to the extracted purified data; and finally, evaluate the performance of the proposed system using different performance measures such as graphical analysis and a statistical test. To verify the consistency of our proposed hybrid system, we use three different ratios for training and testing the dataset. The outcomes show that the proposed hybrid system for ETD is highly accurate and efficient compared to the other models like Alexnet, GRU, Bidirectional Gated Recurrent Unit (BGRU) and Recurrent Neural Network (RNN). |
| first_indexed | 2024-04-24T22:22:09Z |
| format | Article |
| id | doaj.art-1ae7a1e9e9b9418d8f43016d309c3cf8 |
| institution | Directory Open Access Journal |
| issn | 2296-598X |
| language | English |
| last_indexed | 2024-04-24T22:22:09Z |
| publishDate | 2024-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Energy Research |
| spelling | doaj.art-1ae7a1e9e9b9418d8f43016d309c3cf82024-03-20T05:15:39ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2024-03-011210.3389/fenrg.2024.13830901383090Electricity theft detection in smart grid using machine learningHasnain Iftikhar0Hasnain Iftikhar1Nitasha Khan2Muhammad Amir Raza3Ghulam Abbas4Murad Khan5Mouloud Aoudia6Ezzeddine Touti7Ezzeddine Touti8Ahmed Emara9Ahmed Emara10Department of Mathematics, City University of Science and Information Technology, Peshawar, Khyber Pakhtunkhwa, PakistanDepartment of Statistics, Quaid-i-Azam University, Islamabad, PakistanBritish Malaysian Institute, Universiti Kuala Lumpur, Sungai Pusu, MalaysiaDepartment of Electrical Engineering, Mehran University of Engineering and Technology SZAB Campus Khairpur Mir’s, Sindh, PakistanSchool of Electrical Engineering, Southeast University, Nanjing, ChinaDepartment of Statistics, Abdul Wali Khan University, Mardan, PakistanDepartment of Industrial Engineering, College of Engineering, Northern Border University, Arar, Saudi ArabiaDepartment of Electrical Engineering, College of Engineering, Northern Border University, Arar, Saudi ArabiaDepartment of Electrical Engineering, Higher Institute of Applied Sciences and Technology of Kasserine, University of Kairouan, Kairouan, Tunisia0Department of Electrical Engineering, University of Business and Technology, Jeddah, Saudi Arabia1Department of Engineering Mathematics, and Physics, Faculty of Engineering, Alexandria University, Alexandria, EgyptNowadays, electricity theft is a major issue in many countries and poses a significant financial loss for global power utilities. Conventional Electricity Theft Detection (ETD) models face challenges such as the curse of dimensionality and highly imbalanced electricity consumption data distribution. To overcome these problems, a hybrid system Multi-Layer Perceptron (MLP) approach with Gated Recurrent Units (GRU) is proposed in this work. The proposed hybrid system is applied to analyze and solve electricity theft using data from the Chinese National Grid Corporation (CNGC). In the proposed hybrid system, first, preprocess the data; second, balance the data using the k-means Synthetic Minority Oversampling Technique (SMOTE) technique; third, apply the GTU model to the extracted purified data; fourth, apply the MLP model to the extracted purified data; and finally, evaluate the performance of the proposed system using different performance measures such as graphical analysis and a statistical test. To verify the consistency of our proposed hybrid system, we use three different ratios for training and testing the dataset. The outcomes show that the proposed hybrid system for ETD is highly accurate and efficient compared to the other models like Alexnet, GRU, Bidirectional Gated Recurrent Unit (BGRU) and Recurrent Neural Network (RNN).https://www.frontiersin.org/articles/10.3389/fenrg.2024.1383090/fullelectricity theft detectionanomaly detectionsmart gridmachine learningeconomic development |
| spellingShingle | Hasnain Iftikhar Hasnain Iftikhar Nitasha Khan Muhammad Amir Raza Ghulam Abbas Murad Khan Mouloud Aoudia Ezzeddine Touti Ezzeddine Touti Ahmed Emara Ahmed Emara Electricity theft detection in smart grid using machine learning Frontiers in Energy Research electricity theft detection anomaly detection smart grid machine learning economic development |
| title | Electricity theft detection in smart grid using machine learning |
| title_full | Electricity theft detection in smart grid using machine learning |
| title_fullStr | Electricity theft detection in smart grid using machine learning |
| title_full_unstemmed | Electricity theft detection in smart grid using machine learning |
| title_short | Electricity theft detection in smart grid using machine learning |
| title_sort | electricity theft detection in smart grid using machine learning |
| topic | electricity theft detection anomaly detection smart grid machine learning economic development |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2024.1383090/full |
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