A novel technique for detecting electricity theft in secure smart grids using CNN and XG-boost
Electricity theft is one of the main nontechnical losses (NTLs) in distributed networks which cause significant harm to the power grids. As power grids provide the centralized power to all the connected consumers, therefore, any fraudulent consumption can cause harm to the power grids which can dama...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-02-01
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| Series: | Intelligent Systems with Applications |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667305322001053 |
| _version_ | 1811170996554563584 |
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| author | Asif Nawaz Tariq Ali Ghulam Mustafa Saif Ur Rehman Muhammad Rizwan Rashid |
| author_facet | Asif Nawaz Tariq Ali Ghulam Mustafa Saif Ur Rehman Muhammad Rizwan Rashid |
| author_sort | Asif Nawaz |
| collection | DOAJ |
| description | Electricity theft is one of the main nontechnical losses (NTLs) in distributed networks which cause significant harm to the power grids. As power grids provide the centralized power to all the connected consumers, therefore, any fraudulent consumption can cause harm to the power grids which can damage the whole electric power supply and can influence its quality. The detection of such fraudulent consumers becomes difficult when there is a large amount of data. Smart grids can be used to solve this problem as it provides a two-way electricity flow which allows someone to detect, reenact and apply new changes to the electric data flow. The existing systems for electricity theft detection, works on the principle of one dimensional (1-D) electric data, which provides poor accuracy in theft detection. Therefore, an ensemble model based on convolutional neural network and extreme gradient boosting (CNN-XGB) model is presented in this paper. In this model both one dimensional (1-D) and two-dimensional (2-D) electricity consumption data are used to pass to the CNN model. Proposed model achieved the accuracy of 92% for electricity theft detection, which is better than existing models. |
| first_indexed | 2024-04-10T17:06:09Z |
| format | Article |
| id | doaj.art-4da7e23329844d9986813c417b4cb725 |
| institution | Directory Open Access Journal |
| issn | 2667-3053 |
| language | English |
| last_indexed | 2024-04-10T17:06:09Z |
| publishDate | 2023-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Intelligent Systems with Applications |
| spelling | doaj.art-4da7e23329844d9986813c417b4cb7252023-02-06T04:06:25ZengElsevierIntelligent Systems with Applications2667-30532023-02-0117200168A novel technique for detecting electricity theft in secure smart grids using CNN and XG-boostAsif Nawaz0Tariq Ali1Ghulam Mustafa2Saif Ur Rehman3Muhammad Rizwan Rashid4University Institute of Information Technology, PMAS Arid Agriculture University, Rawalpindi, 46000, PakistanUniversity Institute of Information Technology, PMAS Arid Agriculture University, Rawalpindi, 46000, PakistanUniversity Institute of Information Technology, PMAS Arid Agriculture University, Rawalpindi, 46000, PakistanUniversity Institute of Information Technology, PMAS Arid Agriculture University, Rawalpindi, 46000, PakistanCorresponding author.; University Institute of Information Technology, PMAS Arid Agriculture University, Rawalpindi, 46000, PakistanElectricity theft is one of the main nontechnical losses (NTLs) in distributed networks which cause significant harm to the power grids. As power grids provide the centralized power to all the connected consumers, therefore, any fraudulent consumption can cause harm to the power grids which can damage the whole electric power supply and can influence its quality. The detection of such fraudulent consumers becomes difficult when there is a large amount of data. Smart grids can be used to solve this problem as it provides a two-way electricity flow which allows someone to detect, reenact and apply new changes to the electric data flow. The existing systems for electricity theft detection, works on the principle of one dimensional (1-D) electric data, which provides poor accuracy in theft detection. Therefore, an ensemble model based on convolutional neural network and extreme gradient boosting (CNN-XGB) model is presented in this paper. In this model both one dimensional (1-D) and two-dimensional (2-D) electricity consumption data are used to pass to the CNN model. Proposed model achieved the accuracy of 92% for electricity theft detection, which is better than existing models.http://www.sciencedirect.com/science/article/pii/S2667305322001053Convolutional neural networks (CNNs)Deep learningElectricity-theft detectionMachine learning |
| spellingShingle | Asif Nawaz Tariq Ali Ghulam Mustafa Saif Ur Rehman Muhammad Rizwan Rashid A novel technique for detecting electricity theft in secure smart grids using CNN and XG-boost Intelligent Systems with Applications Convolutional neural networks (CNNs) Deep learning Electricity-theft detection Machine learning |
| title | A novel technique for detecting electricity theft in secure smart grids using CNN and XG-boost |
| title_full | A novel technique for detecting electricity theft in secure smart grids using CNN and XG-boost |
| title_fullStr | A novel technique for detecting electricity theft in secure smart grids using CNN and XG-boost |
| title_full_unstemmed | A novel technique for detecting electricity theft in secure smart grids using CNN and XG-boost |
| title_short | A novel technique for detecting electricity theft in secure smart grids using CNN and XG-boost |
| title_sort | novel technique for detecting electricity theft in secure smart grids using cnn and xg boost |
| topic | Convolutional neural networks (CNNs) Deep learning Electricity-theft detection Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2667305322001053 |
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