AN INTELLIGENT HYBRID MODEL FOR REDUCING NON-TECHNICAL LOSSES IN ELECTRICAL INDUSTRY
DOI:
https://doi.org/10.4314/jfas.v12i1.21Keywords:
Fraud detection; Multi agent models; Multi agent ensemble modelsAbstract
Non-technical losses, specially electricity theft is a major concern for electricity distribution companies all over the world due to its huge financial impact on their revenue. For developing countries such as Iran, the case is even more important because of the fact that the use of advanced metering infrastructure have not been implemented completely. Since the data mining and its techniques have been widely used nowadays and proven to be useful in so many cases like fraud detection problems, it has been decided to use this science in order to tackle non-technical losses problem for an Iranian company by focusing on meter tampering or it may also be referred as fraud in electricity which plays a huge role in creating none-technical losses. The proposed model proved to be applicable for the company by having a much better performance using multi agent ensemble model of SVM, RF, and NN, in comparison to the company’s traditional statistical solution which could merely predict less than ten percent of fraud cases correctly.
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References
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[44] https://www.tbtb.ir
[2] Thomas B. Smith. Energy Policy, 2004, 2067–2076, doi: 10.1016/S0301-4215(03)00182-4
[3] Patrick Glauner, Andre Boechat, Lautaro Dolberg, Radu State, Franck Bettinger, Yves Rangoni and Diogo Duarte, Large-Scale Detection of Non-Technical Losses in Imbalanced Data Sets. Power & Energy Society Innovative Smart Grid Technologies Conference, 2016, doi: arXiv:1602.08350
[4] Siemens Energy Services response to Ofgem’s Theft of Electricity and Gas Discussion Document. https://www.ofgem.gov.uk/ofgem-publications/39236/7636-siemens.pdf
[5] Eyad Hashem S.Humaid. A Data Mining Based Fraud Detection Model for Water Consumption Billing System in MOG. Islamic University of Gaza Deanery of higher Studies Information Technology Program Department of Computer Science, 2012
[6] Vitaly Ford, Ambareen Siraj, William Eberle, Symposium on Computational Intelligence Applications in Smart Grid, 2015, doi: 10.1109/CIASG.2014.7011557
[7] Breno C. Costa, Bruno. L. A. Alberto, André M. Portela, W. Maduro, Esdras, IJAIA., 2013, Vol. 4, No. 6, doi: 10.5121/ijaia.2013.4602
[8] F´elix Biscarri, Ignacio Monedero, Carlos Le´on, Ju´an I. Guerrero, Jes´us Biscarri, Roc´ıo Mill´an. A DATA MINING METHOD BASED ON THE VARIABILITY OF THE CUSTOMER CONSUMPTION, a special application on electric utility companies, ICEIS, 2008
[9] F´elix Biscarri, Ignacio Monedero, Carlos Le´on, Ju´an I. Guerrero, Jes´us Biscarri, Roc´ıo Mill´an. A MINING FRAMEWORK TO DETECT NON-TECHNICAL LOSSES IN POWER UTILITIES, ICEIS, 2009, doi: 10.5220/0001953300970102
[10] Juan I. Guerrero, Carlos León, Félix Biscarri, Increasing the efficiency in Non-Technical Losses detection in utility companies, 15th IEEE Mediterranean Electrotechnical Conference, 2010, doi: 10.1109/MELCON.2010.5476320
[11] Jawad Nagi, Keem Siah Yap, Sieh Kiong Tiong, Member, IEEE, Syed Khaleel Ahmed, Member, IEEE, and Malik Mohamad. Nontechnical Loss Detection for Metered Customers in Power Utility Using Support Vector Machines. IEEE TRANSACTIONS ON POWER DELIVERY, 2010, VOL. 25, NO. 2, 1162 – 1171, doi: 10.1109/TPWRD.2009.2030890
[12] DanilloR. Pereira, MarioA. Pazotia, LuísA.M. Pereira, Douglas Rodrigues, CaioO. Ramosd, AndréN. Souzad, JoãoP.Papa, Computers & Electrical Engineering, 2016, 49, 25-38, doi: 10.1016/j.compeleceng.2015.11.001
[13] A. H. Nizar, Student Member, IEEE, Z. Y. Dong, Senior Member, IEEE, and Y. Wang, Senior Member, IEEE, IEEE TRANSACTIONS ON POWER SYSTEMS, 2018, 23, 946 – 955, doi: 10.1109/TPWRS.2008.926431
[14] J. Nagi, K. S. Yap, S. K. Tiong, Member, IEEE, S. K. Ahmed, Member, IEEE, A. M. Mohammad, IEEE Region 10 Conference, 2008, doi: 10.1109/TENCON.2008.4766403
[15] Caio C.O. Ramos, André N. Souza, Giovani Chiachia, Alexandre X. Falcão, João P. Papa, Computers and Electrical Engineering, 2011, 37, 886-894, doi: 10.1016/j.compeleceng.2011.09.013
[16] Anisah Hanim Nizar, Jun Hua Zhao, Zhao Yang Dong, Power System Technology, 2006, doi: 10.1109/ICPST.2006.321964
[17] Rong Jiang, Harry Tagaris, Andrei Lachsz and Mark Jeffrey, Transmission and Distribution Conference and Exhibition, 2002, doi: 10.1109/TDC.2002.1177814
[18] Luis A. M. Pereira, Luis C. S. Afonso, Jo˜ao P. Papa, Zita A. Vale, Caio C. O. Ramos, Danillo S. Gastaldello and Andr´e N. Souza, Innovative Smart Grid Technologies Latin America, 2013, doi: 10.1109/ISGT-LA.2013.6554383
[19] Juan Pablo Kosut, Fernando Santomauro, Andr´es Jorysz, Alicia Fern´andez, Federico Lecumberry, Fernanda Rodr´ıguez, PES Innovative Smart Grid Technologies Latin America, 2015, doi: 10.1109/ISGT-LA.2015.7381272
[20] Monedero I, Biscarri F, Len C, Guerrero JI, Biscarri J, Milln R, Electric Power Energ. Sys. 2012, 34(1), 90–8, doi: 10.1016/j.ijepes.2011.09.009
[21] WenlinHan, YangXiao, computers & security, 2017, 65, 187–201, doi: 10.1016/j.cose.2016.11.009
[22] Deva P Seetharam, Vijay Arya, Dipanjan Chakraborty, Zainul Charbiwala, Tanuja Ganu, Sunil Ghai, Jagabondhu Hazra, Palani Kodeswaran, Rajendu Mitra, Balakrishnan Narayanaswamy, Neha Sengupta, Shivkumar Kalyanaraman, J INDIAN I SCI., 2013, 93
[23] WenlinHan, YangXiao, Computer Networks, 2010, 117, 19-31, doi: 10.1016/j.comnet.2016.10.011
[24] Dmitry Brusilovsky & Eugene Brusilovskiy, DATA MINING: THE MEANS TO COMPETITIVE ADVANTAGE, Business Intelligence solutions, 2008
[25] Lean Yu, Wuyi Yue, Shouyang Wang, K.K. Lai, Expert Systems with Applications, 2010, 37, 1351-1360, doi: 10.1016/j.eswa.2009.06.083
[26] Dustin Boswell, Introduction to Support Vector Machines, 2002
[27] Andy Thomas, An introduction to neural networks for beginners
[28] Hastie T., Tibshirani R., Friedman J. Random Forests,: The Elements of Statistical Learning, Springer, 2009
[29] UCI Machine Learning Repository: Breast Cancer Wisconsin (Original) Data Set (2012) Retrieved 16 Mar 2012, from UCI Machine Learning Repository: Breast Cancer Wisconsin (Original)Dataset: http://archive.ics.uci.edu/ml/datasets/Breast?Cancer ?Wisconsin?(Original)
[30] Prem Melville, Creating Diverse Ensemble Classifiers, The University of Texas at Austin. 2003
[31] Aman Kedia, Manu Narsaria, Saptarsi Goswami, Jayant Taparia, World Journal of Computer Application and Technology, 2017, 5, 1-11, doi: 10.13189/wjcat.2017.050101
[32] Chandra Prasetyo Utomo, Aan Kardiana, Rika Yuliwulandari, IJARAI, 2014, 3, doi: 10.14569/IJARAI.2014.030703
[33] Tarek Helmy, Mosleh M. Al-Harthi, Mohamed T. Faheem, Global Journal of Technology and Optimization. 2012
[34] Anil Narassiguin, Haytham Elghazel, and Alex Aussem, Machine Learning and Knowledge Discovery in Databases, 2017, 10534, 169-186, doi: 10.1007/978-3-319-71249-9_11
[35] Hiba Asria, Hajar Mousannifb, Hassan Al Moatassimec, Thomas Noeld, Procedia Computer Science, 2016, 83, 1064-1069, doi: 10.1016/j.procs.2016.04.224
[36] U. Karthik Kumar, M.B. Sai Nikhil and K. Sumangali, International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials, 2017, doi: 10.1109/ICSTM.2017.8089135
[37] Emina Alickovic, Abdulhamit Subasi, Neural Computing and Applications, 2017, 28, 753–763, doi: 10.1007/s00521-015-2103-9
[38] Do-Yeon Choi, Kwang-Mo Jeong, Dong Hoon Lim, J Health Info. Stat., 2018, 43, 140-147, doi: 10.21032/jhis.2018.43.2.140
[39] Gouda I. Salama, M.B.Abdelhalim, and Magdy Abd-elghany Zeid, International Journal of Computer and Information Technology, 2012, 01, 2277 – 0764
[40] Vikas Chaurasia, Saurabh Pal and BB Tiwari, Journal of Algorithms & Computational Technology, 2018, 12, 119–126, doi: 10.1177/1748301818756225
[41] R. Nithya and B. Santhi, Data Mining and Soft Computing Techniques, 2014, 18-23, doi: 10.5829/idosi.wasj.2014.29.dmsct.4
[42] D.Lavanya and Dr.K.Usha Rani, IJITCS, 2012, 2, doi: 10.5121/ijitcs.2012.2103
[43] M Srinivas, R Bharathy, P Rajalakshmiy and C Krishna Mohan, 17th International Conference on E-health Networking, Application & Services, 2015, doi: 10.1109/HealthCom.2015.7454509
[44] https://www.tbtb.ir
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Published
2019-12-30
How to Cite
SHAHRABI, J.; TAZARVI, H. AN INTELLIGENT HYBRID MODEL FOR REDUCING NON-TECHNICAL LOSSES IN ELECTRICAL INDUSTRY. Journal of Fundamental and Applied Sciences, [S. l.], v. 12, n. 1, p. 329–377, 2019. DOI: 10.4314/jfas.v12i1.21. Disponível em: https://jfas.info/index.php/JFAS/article/view/432. Acesso em: 30 jan. 2025.
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