Theft detection in power utilities using ensemble of chaid decision tree algorithm
Theft of electricity and fraud in energy consumption billing are the primary concerns for Distribution System Operators . Because of those illegal activities, it is believed that billions of dollars are wasted each year. DSOs around the world continue to use conventional time consuming and inefficie...
| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
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2020
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| Online Access: | http://eprints.utm.my/92062/1/MuhammadSalmanSaeed2020_TheftDetectioninPowerUtilitiesusingEnsemble.pdf |
| _version_ | 1796865350480953344 |
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| author | Saeed, Muhammad Salman Mustafa, Mohd. Wazir Sheikh, Usman Ullah Khidrani, Attaullah Mohd., Mohd. Norzali |
| author_facet | Saeed, Muhammad Salman Mustafa, Mohd. Wazir Sheikh, Usman Ullah Khidrani, Attaullah Mohd., Mohd. Norzali |
| author_sort | Saeed, Muhammad Salman |
| collection | ePrints |
| description | Theft of electricity and fraud in energy consumption billing are the primary concerns for Distribution System Operators . Because of those illegal activities, it is believed that billions of dollars are wasted each year. DSOs around the world continue to use conventional time consuming and inefficient methods for non-technical loss detection, particularly in underdeveloped countries . This research work attempts to solve the problems as mentioned above by designing an effective model for detecting electricity theft to classify fraudster customers in a power delivery system. The key motivation for this current study is to support the DSOs in their fight against the stealing of electricity. Initially, the proposed method uses the monthly energy customer consumption data obtained from Multan Electric Power Company (MEPCO) Pakistan to segregate fraudsters and honest customers. The Bagged Chi - square Automatic Interaction Detection (CHAID) based decision tree algorithm is then used to classify the honest and fraudster consumers.Furthermore, based on the mentioned metrics, the performance superiority of the Bagged CHAID-based NTL detection method is validated by comparing its efficacy with that of few well-known state-of-the-art machine learning algorithms such as Artificial Neural Network (ANN), Support Vector Machine (SVM),) Logistic Regression (LR), Discriminant Analysis and Bayesian Network (BN). |
| first_indexed | 2024-03-05T20:55:39Z |
| format | Conference or Workshop Item |
| id | utm.eprints-92062 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-03-05T20:55:39Z |
| publishDate | 2020 |
| record_format | dspace |
| spelling | utm.eprints-920622021-08-30T04:58:21Z http://eprints.utm.my/92062/ Theft detection in power utilities using ensemble of chaid decision tree algorithm Saeed, Muhammad Salman Mustafa, Mohd. Wazir Sheikh, Usman Ullah Khidrani, Attaullah Mohd., Mohd. Norzali TK Electrical engineering. Electronics Nuclear engineering Theft of electricity and fraud in energy consumption billing are the primary concerns for Distribution System Operators . Because of those illegal activities, it is believed that billions of dollars are wasted each year. DSOs around the world continue to use conventional time consuming and inefficient methods for non-technical loss detection, particularly in underdeveloped countries . This research work attempts to solve the problems as mentioned above by designing an effective model for detecting electricity theft to classify fraudster customers in a power delivery system. The key motivation for this current study is to support the DSOs in their fight against the stealing of electricity. Initially, the proposed method uses the monthly energy customer consumption data obtained from Multan Electric Power Company (MEPCO) Pakistan to segregate fraudsters and honest customers. The Bagged Chi - square Automatic Interaction Detection (CHAID) based decision tree algorithm is then used to classify the honest and fraudster consumers.Furthermore, based on the mentioned metrics, the performance superiority of the Bagged CHAID-based NTL detection method is validated by comparing its efficacy with that of few well-known state-of-the-art machine learning algorithms such as Artificial Neural Network (ANN), Support Vector Machine (SVM),) Logistic Regression (LR), Discriminant Analysis and Bayesian Network (BN). 2020 Conference or Workshop Item PeerReviewed application/pdf en http://eprints.utm.my/92062/1/MuhammadSalmanSaeed2020_TheftDetectioninPowerUtilitiesusingEnsemble.pdf Saeed, Muhammad Salman and Mustafa, Mohd. Wazir and Sheikh, Usman Ullah and Khidrani, Attaullah and Mohd., Mohd. Norzali (2020) Theft detection in power utilities using ensemble of chaid decision tree algorithm. In: 4th Asia International Multidisciplinary Conference 2020, 17 - 19 Apr 2020, Skudai, Malaysia. http://dx.doi.org/10.31580/sps.v2i2.1480 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Saeed, Muhammad Salman Mustafa, Mohd. Wazir Sheikh, Usman Ullah Khidrani, Attaullah Mohd., Mohd. Norzali Theft detection in power utilities using ensemble of chaid decision tree algorithm |
| title | Theft detection in power utilities using ensemble of chaid decision tree algorithm |
| title_full | Theft detection in power utilities using ensemble of chaid decision tree algorithm |
| title_fullStr | Theft detection in power utilities using ensemble of chaid decision tree algorithm |
| title_full_unstemmed | Theft detection in power utilities using ensemble of chaid decision tree algorithm |
| title_short | Theft detection in power utilities using ensemble of chaid decision tree algorithm |
| title_sort | theft detection in power utilities using ensemble of chaid decision tree algorithm |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://eprints.utm.my/92062/1/MuhammadSalmanSaeed2020_TheftDetectioninPowerUtilitiesusingEnsemble.pdf |
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