An Efficient boosted C5.0 decision-tree-based classification approach for detecting non-technical losses in power utilities
Electricity fraud in billing are the primary concerns for Distribution System Operators (DSO). It is estimated that billions of dollars are wasted annually due to these illegal activities. DSOs around the world, especially in underdeveloped countries, still utilize conventional time consuming and in...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020
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| Online Access: | http://eprints.utm.my/90888/1/MohdWazirMustafa2020_AnEfficientBoostedC5.0DecisionTreeBasedClassification.pdf |
| _version_ | 1796865119375851520 |
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| author | Saeed, Muhammad Salman Mustafa, Mohd. Wazir Ullah Sheikh, Usman Ahmed Jumani, Touqeer Khan, Ilyas Atawneh, Samer Hamadneh, Nawaf N. |
| author_facet | Saeed, Muhammad Salman Mustafa, Mohd. Wazir Ullah Sheikh, Usman Ahmed Jumani, Touqeer Khan, Ilyas Atawneh, Samer Hamadneh, Nawaf N. |
| author_sort | Saeed, Muhammad Salman |
| collection | ePrints |
| description | Electricity fraud in billing are the primary concerns for Distribution System Operators (DSO). It is estimated that billions of dollars are wasted annually due to these illegal activities. DSOs around the world, especially in underdeveloped countries, still utilize conventional time consuming and inefficient methods for Non-Technical Loss (NTL) detection. This research work attempts to solve the mentioned problem by developing an efficient energy theft detection model in order to identify the fraudster customers in a power distribution system. The key motivation for the present study is to assist the DSOs in their fight against energy theft. The proposed computational model initially utilizes a set of distinct features extracted from the monthly consumers' consumption data, obtained from Multan Electric Power Company (MEPCO) Pakistan, to segregate the honest and the fraudulent customers. The Pearson's chi-square feature selection algorithm is adopted to select the most relevant features among the extracted ones. Finally, the Boosted C5.0 Decision Tree (DT) algorithm is used to classify the honest and the fraudster consumers based on the outcomes of the selected features. To validate the superiority of the proposed NTL detection approach, its performance is matched with that of few state-of-the-art machine learning algorithms (one of most exciting recent technologies in Artificial Intelligence), like Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Network (ANN) and Extreme Gradient Bossting (XGBoost). The proposed NTL detection method provides an accuracy of 94.6%, Sensitivity of 78.1%, Specificity of 98.2%, F1 score 84.9% and Precision of 93.2% which are significantly higher than that of the same for the above-mentioned algorithms. |
| first_indexed | 2024-03-05T20:52:06Z |
| format | Article |
| id | utm.eprints-90888 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-03-05T20:52:06Z |
| publishDate | 2020 |
| publisher | MDPI AG |
| record_format | dspace |
| spelling | utm.eprints-908882021-05-31T13:22:09Z http://eprints.utm.my/90888/ An Efficient boosted C5.0 decision-tree-based classification approach for detecting non-technical losses in power utilities Saeed, Muhammad Salman Mustafa, Mohd. Wazir Ullah Sheikh, Usman Ahmed Jumani, Touqeer Khan, Ilyas Atawneh, Samer Hamadneh, Nawaf N. TK Electrical engineering. Electronics Nuclear engineering Electricity fraud in billing are the primary concerns for Distribution System Operators (DSO). It is estimated that billions of dollars are wasted annually due to these illegal activities. DSOs around the world, especially in underdeveloped countries, still utilize conventional time consuming and inefficient methods for Non-Technical Loss (NTL) detection. This research work attempts to solve the mentioned problem by developing an efficient energy theft detection model in order to identify the fraudster customers in a power distribution system. The key motivation for the present study is to assist the DSOs in their fight against energy theft. The proposed computational model initially utilizes a set of distinct features extracted from the monthly consumers' consumption data, obtained from Multan Electric Power Company (MEPCO) Pakistan, to segregate the honest and the fraudulent customers. The Pearson's chi-square feature selection algorithm is adopted to select the most relevant features among the extracted ones. Finally, the Boosted C5.0 Decision Tree (DT) algorithm is used to classify the honest and the fraudster consumers based on the outcomes of the selected features. To validate the superiority of the proposed NTL detection approach, its performance is matched with that of few state-of-the-art machine learning algorithms (one of most exciting recent technologies in Artificial Intelligence), like Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Network (ANN) and Extreme Gradient Bossting (XGBoost). The proposed NTL detection method provides an accuracy of 94.6%, Sensitivity of 78.1%, Specificity of 98.2%, F1 score 84.9% and Precision of 93.2% which are significantly higher than that of the same for the above-mentioned algorithms. MDPI AG 2020-06 Article PeerReviewed application/pdf en http://eprints.utm.my/90888/1/MohdWazirMustafa2020_AnEfficientBoostedC5.0DecisionTreeBasedClassification.pdf Saeed, Muhammad Salman and Mustafa, Mohd. Wazir and Ullah Sheikh, Usman and Ahmed Jumani, Touqeer and Khan, Ilyas and Atawneh, Samer and Hamadneh, Nawaf N. (2020) An Efficient boosted C5.0 decision-tree-based classification approach for detecting non-technical losses in power utilities. Energies, 13 (12). p. 3242. ISSN 1996-1073 http://dx.doi.org/10.3390/en13123242 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Saeed, Muhammad Salman Mustafa, Mohd. Wazir Ullah Sheikh, Usman Ahmed Jumani, Touqeer Khan, Ilyas Atawneh, Samer Hamadneh, Nawaf N. An Efficient boosted C5.0 decision-tree-based classification approach for detecting non-technical losses in power utilities |
| title | An Efficient boosted C5.0 decision-tree-based classification approach for detecting non-technical losses in power utilities |
| title_full | An Efficient boosted C5.0 decision-tree-based classification approach for detecting non-technical losses in power utilities |
| title_fullStr | An Efficient boosted C5.0 decision-tree-based classification approach for detecting non-technical losses in power utilities |
| title_full_unstemmed | An Efficient boosted C5.0 decision-tree-based classification approach for detecting non-technical losses in power utilities |
| title_short | An Efficient boosted C5.0 decision-tree-based classification approach for detecting non-technical losses in power utilities |
| title_sort | efficient boosted c5 0 decision tree based classification approach for detecting non technical losses in power utilities |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://eprints.utm.my/90888/1/MohdWazirMustafa2020_AnEfficientBoostedC5.0DecisionTreeBasedClassification.pdf |
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