Identification of the characteristics incident to the detection of non-technical losses for two Colombian energy companies
DOI:
https://doi.org/10.17533/udea.redin.n84a08Keywords:
non-technical losses, MDS, cluster, Benford's Law, decision treesAbstract
The study of non-technical losses affecting energy trading companies has guided the researchers’ perspective on different techniques and tools that allow them to detect, and why not, to forecast such losses. In the search for a solution to the problem, the different researchers rely on variables that, in many cases, the same marketing companies, from their practical experience, have been considered as incidents in the identification of the problem. However, most of the studies carried out do not support their solutions with the fact that each trading company retains particular data in which both, technical and socio-economic characteristics recorded, are not necessarily shared in their databases. In this work, we follow up on some of the characteristics registered by two Colombian energy trading companies, which serve two different regions of the country in terms of topography and idiosyncrasy. In particular, attention is focused on two characteristics measured in both companies, which by their nature, will always be on the data of any energy trading company: Consumption in kWh, and the period, measured in months. For this purpose, Benford curves analysis, MultiDimensional Scaling (MDS), and hierarchical cluster will be implemented. Finally, it will be studied if the incidence of the variables visualized in the studies presented is reflected in the decision tree model.
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J. I. Guerrero, C. León, I. Monedero, F. Biscarri, and J. Biscarri, “Improving Knowledge-Based systems with statistical techniques, text mining, and neural networks for non-technical loss detection,” Knowledge-Based Systems, vol. 71, pp. 376-388, 2014.
J. P. Navani, N. K. Sharma, and S. Sapra, “Technical and Non-Technical Losses in Power System and Its Economic Consequence in Indian Economy,” IJECSE, vol. 1. no. 2, pp. 757-761, 2003.
P. Glauner et al., Large-Scale Detection of Non-Technical Losses in Imbalanced Data Sets, 2016. [Online]. Available: https://arxiv.org/pdf/1602.08350.pdf. Accessed on: Mar. 13, 2016.
P. Glauner, J. Meira, P. Valtchev, R. State, and F. Bettinger, The Challenge of Non-Technical Loss Detection using Artificial Intelligence: A Survey, 2017. [Online]. Available: https://arxiv.org/pdf/1606.00626.pdf. Accessed on: Jun. 5, 2016.
Comisión de Regulación de Energía y Gas (CREG), Propuesta para remunerar planes de reducción de pérdidas no tecnicas de energía electrica en sistemas de distribución local, CREG, Bógota, Colombia, Jan. 2011.
J. Nagi, K. S. Yap, S. K. Tiong, S. K. Ahmed, and M. Mohammad, “Detection of Abnormalities and Electricity Theft using Genetic Support Vector Machines,” in IEEE Region 10 Conference TENCON, Hyderabad, India, 2008, pp. 1-6.
R. Jiang et al., “Energy-Theft Detection Issues for Advanced Metering Infrastructure in Smart Grid,” Tsinghua Science and Technology, vol. 19, no. 2, pp. 105- 120, 2014.
B. Baesens, V. Vlasselaer, and W. Verbeke, Fraud Analytics Using Descriptive, Predictive, and social Network techniques. A guide to data Science for Fraud Detection, New Jersey, USA: Wiley, 2015.
J. P. Navani, N. K. Sharma, and S. Sapra, “Analysis of Technical and Non Technical Losses in Power System and its Economic Consequences in Power Sector,” International Journal of Advances in Electrical and Electronics Engineering IJAEEE, vol 1, no 3, pp. 396-405, 2014. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.227.4747&rep=rep1&type=pdf. Accesed on: Aug. 28, 2017.
O. Refou, Q. Alsafasfeh, and M. Alsoud, “Evaluation of Electric Energy Losses in Southern Governorates of Jordan Distribution Electric System,” International Journal of Energy Engineering, vol. 5, no. 2, pp. 25-33, 2015.
J. Nagi, K. S. Yap, S. K. Tiong, S. K. Ahmed, and M. Mohammad, “Nontechnical Loss Detection for Metered Customers in Power Utility Using Support Vector Machines,” IEEE Transactions on Power Delivery, vol. 25, no. 2, pp. 1162-1171, 2010.
E. A. Aranha and J. Coelho, “Probabilistic methodology for Technical and Non-Technical Losses estimation in distribution system,” Electric Power Systems Research, vol. 97, pp. 93-99, 2013.
L. T. Faria, J. D. Melo, and A. Padilha, “SpatialTemporal Estimation for Nontechnical Losses,” IEEE Transactions on Power Delivery, vol. 31, no. 1, pp. 362- 369, 2016.
C. C. Ramos, A. N. Souza, G. Chiachia, A. X. Falcão, and J. P. Papa, “A novel algorithm for feature selection using Harmony Search and its application for nontechnical losses detection,” Computers & Electrical Engineering, vol. 37, no. 6, pp. 886-894, 2011.
I. Borg and P. J. Groenen, Modern Multidimensional Scaling: Theory and Applications, 2nd ed. New York, USA: Springer, 2005.
I. Borg, P. J. Groenen, and P. Mair, Applied Multidimensional Scaling, 2nd ed. New York, USA: Springer, 2013.
T. F. Cox and M. A. Cox, Multidimensional Scaling, 2nd ed. New York, USA: Chapman & Hall, 2000.
N. Ye, Data Mining. Theories, algorithms, and examples, Boca Raton, USA: CRC Press, 2014.
G. Williams, Data Mining with Rattle and R: The Art of Excavating Data for Knowledge Discovery, New York, USA: Springer, 2011.
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