Electricity theft detection in Power utilities using Bagged CHAID-Based classification Trees
الموضوعات :
Muhammad Saeed
1
(School of Electrical Engineering, University Technology Malaysia, Skudai, Johor Bahru 81310, Malaysia)
Mohd. Wazir Mustafa
2
(School of Electrical Engineering, University Technology Malaysia, Skudai, Johor Bahru 81310, Malaysia)
Usman Sheikh
3
(School of Electrical Engineering, University Technology Malaysia, Skudai, Johor Bahru 81310, Malaysia)
Attaullah Khidrani
4
(School of Electrical Engineering, University Technology Malaysia, Skudai, Johor Bahru 81310, Malaysia)
Mohd Norzali Haji Mohd
5
(School of Electrical Engineering, University Technology Malaysia, Skudai, Johor Bahru 81310, Malaysia)
الکلمات المفتاحية: Electricity theft, fraud billing, Non-Technical Loss, Chi-square Automatic Interaction Detection,
ملخص المقالة :
Electricity theft and fraud in billing are the primary concerns for Distribution System Operators (DSO). It is estimated that billions of dollars are lost each year due to these illegal activities. DSOs around the world, especially in underdeveloped countries, are still utilizing conventional time consuming and inefficient methods for Non-Technical Loss (NTL) detection. This research work attempts to solve the mentioned problems by developing an efficient energy theft detection model to identify the fraudster customers in a power distribution system. The key motivation for the current study is to assist the DSOs for their campaign against energy theft. The proposed method initially utilizes the monthly consumption data of energy customers, obtained from Multan Electric Power Company (MEPCO) Pakistan, to segregate the honest and the fraudulent customers. The Bagged Chi-square Automatic Interaction Detection (CHAID) Decision Tree (DT) algorithm is 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 Support Vector Machine (SVM), Artificial Neural Network (ANN) Logistic Regression (LR), Bayesian Network (BN) and Discriminant Analysis. The proposed NTL detection method provides an Accuracy of 86.35% and Area Under Curve (AUC) of 0.927, respectively, which are significantly higher than that of the same for the mentioned algorithms.
Angelos, E. W. S., Saavedra, O. R., Cortés, O. A. C., & De Souza, A. N. (2011). Detection and identification of abnormalities in customer consumptions in power distribution systems. IEEE Transactions on Power Delivery, 26(4), 2436-2442.
Antonelo, E. A., & State, R. (2019, October). On importance weighting for electric fraud detection with dataset shifts. In 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC) (pp. 3235-3242). IEEE.
Baskar, P., Joseph, M. A., Narayanan, N., & Loya, R. B. (2013, April). Experimental investigation of oxygen enrichment on performance of twin cylinder diesel engine with variation of injection pressure. In 2013 International Conference on Energy Efficient Technologies for Sustainability (pp. 682-687). IEEE.
Belloli, M., Melzi, S., Negrini, S., & Squicciarini, G. (2010). Numerical analysis of the dynamic response of a 5-conductor expanded bundle subjected to turbulent wind. IEEE transactions on power delivery, 25(4), 3105-3112.
Bonte, C., & Vercauteren, F. (2018). Privacy-preserving logistic regression training. BMC medical genomics, 11(4), 13-21.
Bradley, A. P. (1997). The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern recognition, 30(7), 1145-1159.
Cárdenas, A. A., Amin, S., Schwartz, G., Dong, R., & Sastry, S. (2012, October). A game theory model for electricity theft detection and privacy-aware control in AMI systems. In 2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton) (pp. 1830-1837). IEEE.
Costa, B. C., Alberto, B. L., Portela, A. M., Maduro, W., & Eler, E. O. (2013). Fraud detection in electric power distribution networks using an ann-based knowledge-discovery process. International Journal of Artificial Intelligence & Applications, 4(6), 17.
Depuru, S. S. S. R., Wang, L., Devabhaktuni, V., & Gudi, N. (2011, March). Smart meters for power grid—Challenges, issues, advantages and status. In 2011 IEEE/PES Power Systems Conference and Exposition (pp. 1-7). IEEE.
Ford, V., Siraj, A., & Eberle, W. (2014, December). Smart grid energy fraud detection using artificial neural networks. In 2014 IEEE symposium on computational intelligence applications in smart grid (CIASG) (pp. 1-6). IEEE.
Ghori, K. M., Abbasi, R. A., Awais, M., Imran, M., Ullah, A., & Szathmary, L. (2019). Performance analysis of different types of machine learning classifiers for non-technical loss detection. IEEE Access, 8, 16033-16048.
Hosseini, S., & Barker, K. (2016). Modeling infrastructure resilience using Bayesian networks: A case study of inland waterway ports. Computers & Industrial Engineering, 93, 252-266.
Jain, M. B., Srinivas, M. B., & Jain, A. (2008, October). A novel web based expert system architecture for on-line and off-line fault diagnosis and control (FDC) of power system equipment. In 2008 Joint International Conference on Power System Technology and IEEE Power India Conference (pp. 1-5). IEEE.
Jamil, F., & Ahmad, E. (2019). Policy considerations for limiting electricity theft in the developing countries. Energy policy, 129, 452-458.
Jiang, R., Lu, R., Wang, Y., Luo, J., Shen, C., & Shen, X. (2014). Energy-theft detection issues for advanced metering infrastructure in smart grid. Tsinghua Science and Technology, 19(2), 105-120.
Jokar, P. (2015). Detection of malicious activities against advanced metering infrastructure in smart grid (Doctoral dissertation, University of British Columbia).
Jokar, P., Arianpoo, N., & Leung, V. C. (2015). Electricity theft detection in AMI using customers’ consumption patterns. IEEE Transactions on Smart Grid, 7(1), 216-226.
Kessides, I. N. (2013). Chaos in power: Pakistan's electricity crisis. Energy policy, 55, 271-285.
Kim, J., Caire, G., & Molisch, A. F. (2015). Quality-aware streaming and scheduling for device-to-device video delivery. IEEE/ACM Transactions on Networking, 24(4), 2319-2331.
Liu, Y., & Hu, S. (2015). Cyberthreat analysis and detection for energy theft in social networking of smart homes. IEEE Transactions on Computational Social Systems, 2(4), 148-158.
McLaughlin, S., Holbert, B., Fawaz, A., Berthier, R., & Zonouz, S. (2013). A multi-sensor energy theft detection framework for advanced metering infrastructures. IEEE Journal on Selected Areas in Communications, 31(7), 1319-1330.
Messinis, G. M., Rigas, A. E., & Hatziargyriou, N. D. (2019). A hybrid method for non-technical loss detection in smart distribution grids. IEEE Transactions on Smart Grid, 10(6), 6080-6091.
Micheli, G., Soda, E., Vespucci, M. T., Gobbi, M., & Bertani, A. (2019). Big data analytics: an aid to detection of non-technical losses in power utilities. Computational Management Science, 16(1), 329-343.
Mohammad, N., Barua, A., & Arafat, M. A. (2013, February). A smart prepaid energy metering system to control electricity theft. In 2013 International Conference on Power, Energy and Control (ICPEC) (pp. 562-565). IEEE.
Nagi, J., Yap, K. S., Tiong, S. K., Ahmed, S. K., & Mohamad, M. (2009). Nontechnical loss detection for metered customers in power utility using support vector machines. IEEE transactions on Power Delivery, 25(2), 1162-1171.
Navani, J. P., Sharma, N. K., & Sapra, S. (2012). Technical and non-technical losses in power system and its economic consequence in Indian economy. International journal of electronics and computer science engineering, 1(2), 757-761.
Otuoze, A. O., Mustafa, M. W., Mohammed, O. O., Saeed, M. S., Surajudeen‐Bakinde, N. T., & Salisu, S. (2019). Electricity theft detection by sources of threats for smart city planning. IET Smart Cities, 1(2), 52-60.
Ramos, C. C. O., de Sousa, A. N., Papa, J. P., & Falcao, A. X. (2010). A new approach for nontechnical losses detection based on optimum-path forest. IEEE Transactions on Power Systems, 26(1), 181-189.
Saeed, M. S., Mustafa, M. W., Hamadneh, N. N., Alshammari, N. A., Sheikh, U. U., Jumani, T. A., ... & Khan, I. (2020). Detection of non-technical losses in power utilities—A comprehensive systematic review. Energies, 13(18), 4727.
Saeed, M. S., Mustafa, M. W., Sheikh, U. U., Jumani, T. A., & Mirjat, N. H. (2019). Ensemble bagged tree based classification for reducing non-technical losses in multan electric power company of Pakistan. Electronics, 8(8), 860.
Saeed, M. S., Mustafa, M. W., Sheikh, U. U., Khidrani, A., & Mohd, M. N. H. (2020). THEFT DETECTION IN POWER UTILITIES USING ENSEMBLE OF CHAID DECISION TREE ALGORITHM. Science Proceedings Series, 2(2), 161-165.
Saeed, M. S., Mustafa, M., Bin, W., Sheikh, U. U., Salisu, S., & Mohammed, O. O. (2020). Fraud detection for metered costumers in power distribution companies using C5. 0 decision tree algorithm. Journal of Computational and Theoretical Nanoscience, 17(2-3), 1318-1325.
Salman Saeed, M., Mustafa, M. W., Sheikh, U. U., Jumani, T. A., Khan, I., Atawneh, S., & Hamadneh, N. N. (2020). An efficient boosted C5. 0 decision-tree-based classification approach for detecting non-technical losses in power utilities. Energies, 13(12), 3242.
Singh, S. K., Bose, R., & Joshi, A. (2018). Entropy-based electricity theft detection in AMI network. IET Cyber-Physical Systems: Theory & Applications, 3(2), 99-105.
Singh, S. K., Bose, R., & Joshi, A. (2018, February). Energy theft detection in advanced metering infrastructure. In 2018 IEEE 4th World Forum on Internet of Things (WF-IoT) (pp. 529-534). IEEE.
Singh, S. K., Bose, R., & Joshi, A. (2018, February). Minimizing energy theft by statistical distance based theft detector in ami. In 2018 Twenty Fourth National Conference on Communications (NCC) (pp. 1-5). IEEE.
Stamenković, M., Steinwall, E., Nilsson, A. K., & Wulff, A. (2020). Fatty acids as chemotaxonomic and ecophysiological traits in green microalgae (desmids, Zygnematophyceae, Streptophyta): a discriminant analysis approach. Phytochemistry, 170, 112200.
Tso, G. K., & Yau, K. K. (2007). Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks. Energy, 32(9), 1761-1768.
Yip, S. C., Tan, W. N., Tan, C., Gan, M. T., & Wong, K. (2018). An anomaly detection framework for identifying energy theft and defective meters in smart grids. International Journal of Electrical Power & Energy Systems, 101, 189-203.
Yip, S. C., Wong, K., Hew, W. P., Gan, M. T., Phan, R. C. W., & Tan, S. W. (2017). Detection of energy theft and defective smart meters in smart grids using linear regression. International Journal of Electrical Power & Energy Systems, 91, 230-240.
Zhang, X. P., & Cheng, X. M. (2009). Energy consumption, carbon emissions, and economic growth in China. Ecological economics, 68(10), 2706-2712.
Zheng, K., Chen, Q., Wang, Y., Kang, C., & Xia, Q. (2018). A novel combined data-driven approach for electricity theft detection. IEEE Transactions on Industrial Informatics, 15(3), 1809-1819.
