Theft detection dataset for benchmarking and machine learning based classification in a smart grid environment
Smart meters are key elements of a smart grid. These data from Smart Meters can help us analyze energy consumption behaviour. The machine learning and deep learning approaches can be used for mining the hidden theft detection information in the smart meter data. However, it needs effective data extr...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-01-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157822001562 |
| _version_ | 1797942150939279360 |
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| author | Salah Zidi Alaeddine Mihoub Saeed Mian Qaisar Moez Krichen Qasem Abu Al-Haija |
| author_facet | Salah Zidi Alaeddine Mihoub Saeed Mian Qaisar Moez Krichen Qasem Abu Al-Haija |
| author_sort | Salah Zidi |
| collection | DOAJ |
| description | Smart meters are key elements of a smart grid. These data from Smart Meters can help us analyze energy consumption behaviour. The machine learning and deep learning approaches can be used for mining the hidden theft detection information in the smart meter data. However, it needs effective data extraction. This research presents a theft detection dataset (TDD2022) and a machine learning-based solution for automated theft identification in a smart grid environment. An effective theft generator is modelled and used for obtaining a multi-class theft detection dataset from publicly available consumer energy consumption data, owned by the “Open Energy Data Initiative” (OEDI) platform. This is an important and interesting phase to explore in the smart grid field. The proposed dataset can be used for benchmarking and comparative studies. We evaluated the proposed dataset using five different machine learning techniques: k-nearest neighbours (KNN), decision trees (DT), random forest (RF), bagging ensemble (BE), and artificial neural networks (ANN) with different evaluation alternatives (mechanisms). Overall, our best empirical results have been recorded to the theft detection-based RF model scoring an improvement in the performance metrics by 10% or more over the other developed models. |
| first_indexed | 2024-04-10T20:02:47Z |
| format | Article |
| id | doaj.art-a72199145fd74784b18d6f4ef35d3e4d |
| institution | Directory Open Access Journal |
| issn | 1319-1578 |
| language | English |
| last_indexed | 2024-04-10T20:02:47Z |
| publishDate | 2023-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj.art-a72199145fd74784b18d6f4ef35d3e4d2023-01-27T04:18:39ZengElsevierJournal of King Saud University: Computer and Information Sciences1319-15782023-01-013511325Theft detection dataset for benchmarking and machine learning based classification in a smart grid environmentSalah Zidi0Alaeddine Mihoub1Saeed Mian Qaisar2Moez Krichen3Qasem Abu Al-Haija4Hatem Bettaher Laboratory (IRESCOMATH), University of Gabes, Gabes, TunisiaDepartment of Management Information Systems and Production Management, College of Business and Economics, Qassim University, P.O. Box: 6640, Buraidah 51452, Saudi Arabia; Corresponding author.Department of Electrical and Computer Engineering, Effat University, 22332 Jeddah, Saudi ArabiaFaculty of CSIT, Al-Baha University, Saudi Arabia and ReDCAD Laboratory, University of Sfax, TunisiaDepartment of Computer Science/Cybersecurity, Princess Sumaya University for Technology (PSUT), Amman 11941, JordanSmart meters are key elements of a smart grid. These data from Smart Meters can help us analyze energy consumption behaviour. The machine learning and deep learning approaches can be used for mining the hidden theft detection information in the smart meter data. However, it needs effective data extraction. This research presents a theft detection dataset (TDD2022) and a machine learning-based solution for automated theft identification in a smart grid environment. An effective theft generator is modelled and used for obtaining a multi-class theft detection dataset from publicly available consumer energy consumption data, owned by the “Open Energy Data Initiative” (OEDI) platform. This is an important and interesting phase to explore in the smart grid field. The proposed dataset can be used for benchmarking and comparative studies. We evaluated the proposed dataset using five different machine learning techniques: k-nearest neighbours (KNN), decision trees (DT), random forest (RF), bagging ensemble (BE), and artificial neural networks (ANN) with different evaluation alternatives (mechanisms). Overall, our best empirical results have been recorded to the theft detection-based RF model scoring an improvement in the performance metrics by 10% or more over the other developed models.http://www.sciencedirect.com/science/article/pii/S1319157822001562Smart meter dataEnergy consumptionTheft detectionTheft generatorMachine learning |
| spellingShingle | Salah Zidi Alaeddine Mihoub Saeed Mian Qaisar Moez Krichen Qasem Abu Al-Haija Theft detection dataset for benchmarking and machine learning based classification in a smart grid environment Journal of King Saud University: Computer and Information Sciences Smart meter data Energy consumption Theft detection Theft generator Machine learning |
| title | Theft detection dataset for benchmarking and machine learning based classification in a smart grid environment |
| title_full | Theft detection dataset for benchmarking and machine learning based classification in a smart grid environment |
| title_fullStr | Theft detection dataset for benchmarking and machine learning based classification in a smart grid environment |
| title_full_unstemmed | Theft detection dataset for benchmarking and machine learning based classification in a smart grid environment |
| title_short | Theft detection dataset for benchmarking and machine learning based classification in a smart grid environment |
| title_sort | theft detection dataset for benchmarking and machine learning based classification in a smart grid environment |
| topic | Smart meter data Energy consumption Theft detection Theft generator Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S1319157822001562 |
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