An Efficient Boosted C5.0 Decision-Tree-Based Classification Approach for Detecting Non-Technical Losses in Power Utilities

Muhammad Salman Saeed, Mohd Wazir Mustafa, Usman Ullah Sheikh, Touqeer Ahmed Jumani, Ilyas Khan, Samer Atawneh and Nawaf N. Hamadneh
Additional contact information
Muhammad Salman Saeed: School of Electrical Engineering, Universiti Teknologi Malaysia, Skudai, Johor Bahru 81310, Malaysia
Mohd Wazir Mustafa: School of Electrical Engineering, Universiti Teknologi Malaysia, Skudai, Johor Bahru 81310, Malaysia
Usman Ullah Sheikh: School of Electrical Engineering, Universiti Teknologi Malaysia, Skudai, Johor Bahru 81310, Malaysia
Touqeer Ahmed Jumani: School of Electrical Engineering, Universiti Teknologi Malaysia, Skudai, Johor Bahru 81310, Malaysia
Ilyas Khan: Faculty of Mathematics and Statistics, Ton Duc Thang University, Ho Chi Minh City 72915, Vietnam
Samer Atawneh: College of Computing and Informatics, Saudi Electronic University, Riyadh 11673, Saudi Arabia
Nawaf N. Hamadneh: Department of Basic Sciences, College of Science and Theoretical Studies, Saudi Electronic University, Riyadh 11673, Saudi Arabia

Energies, 2020, vol. 13, issue 12, 1-19

Abstract: 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.

Keywords: machine learning algorithms; artificial intelligence; electricity theft detection; boosted C5.0 decision tree; power utilities (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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