Electricity theft detection in Power utilities using Bagged CHAID-Based classification Trees
Subject Areas : Environmental ManagementMuhammad Saeed 1 , Mohd. Wazir Mustafa 2 , Usman Sheikh 3 , Attaullah Khidrani 4 , Mohd Norzali Haji Mohd 5
1 - School of Electrical Engineering, University Technology Malaysia, Skudai, Johor Bahru 81310, Malaysia
2 - School of Electrical Engineering, University Technology Malaysia, Skudai, Johor Bahru 81310, Malaysia
3 - School of Electrical Engineering, University Technology Malaysia, Skudai, Johor Bahru 81310, Malaysia
4 - School of Electrical Engineering, University Technology Malaysia, Skudai, Johor Bahru 81310, Malaysia
5 - School of Electrical Engineering, University Technology Malaysia, Skudai, Johor Bahru 81310, Malaysia
Keywords:
Abstract :
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.