Abstract
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).
Highlights
Graphical AbstractWe show the Decision Tree (DT) formed by the Chi - square Automatic Interaction Detection (CHAID) algorithm for the classification of honest and fraudster customers based on the difference between energy consumption
The Chi - square Automatic Interaction Detection (CHAID) belongs to the group of the classification decision tree
The Bagged Chi - square Automatic Interaction Detection (CHAID) based decision tree algorithm is utilized to segregate the fraudster and genuine consumers.based on the standard performance measuring metrics, the superiority of the Bagged CHAID-based NTL detection method is validated by comparing its efficacy with that of the well-known machine learning algorithms such as Logistic Regression (LR), Support Vector Machine (SVM), Artificial Neural Network (ANN), Discriminant Analysis and Bayesian Network (BN)
Summary
We show the DT formed by the CHAID algorithm for the classification of honest and fraudster customers based on the difference between energy consumption. This study proposes an Artificial-Intelligence based method called Bagged CHAID Decision tree algorithm for energy theft detection. The proposed scheme will generate a list of fraudulent customers which will help to effectively classify the fraudulent consumers. The Bagged CHAID Decision tree algorithm based classification approach makes use of MEPCO Multan, Pakistan's energy consumption data to classify the fraudster and honest customer. At the final stage of the proposed method, shortlisted potential fraudsters customers must be inspected onsite to catch the perpetrators effectively
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