Identification of the characteristics incident to the detection of non-technical losses for two Colombian energy companies
AbstractThe 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.
Aranha Neto, E. A., & Coelho, J. (2013). Probabilistic methodology for Technical and Non-Technical Losses estimation in distribution system. Electric Power Systems Research 97 , 93-99.
Baesens, B., Van Vlasselaer, V., & Verbeke, W. (2015). Fraud Analytics Using Descriptive, Predictive, and social Network techniques. A guide to data Science for Fraud Detection. New Jersey: Wiley.
Borg , I., & Groenen, P. J. (2005). Modern Multidimensional Scaling: Theory and Applications (Second Edition). New York: Springer.
Borg, I., Groenen, P. J., & Patrick, M. (2013). Applied Multidimensional Scaling. 2a Edición. New York: Springer.
Cox, T. F., & Cox, M. A. (2000). Multidimensional Scaling 2da edición. New York: Chapman.
CREG, C. d. (2011). Cartilla: Propuesta para remunera planes de reducción de pérdidas no tecnicas de energía electrica en sistemas de distribución local. Bógota: CREG.
Faria, L. T., David, J., & Padilha-Feltrin, A. (2016). Spatial-Temporal Estimation for Nontechnical Losses. IEEE TRANSACTIONS ON POWER DELIVERY, VOL. 31, NO. 1,, 362-369.
Glauner, P. B. (2016). Large-Scale Detection of Non-Technical Losses in Imbalanced Data Sets. arXiv1602.08350v1.
Glauner, P., Boechat, A., Dolberg, L., Meira, J., State, R., Bettinger, F., et al. (2016). The Challenge of Non-Technical Loss Detection using Artificial Intelligence: A Survey. A Survey." arXiv preprint arXiv:1606.00626.
Guerrero, J. I., León, C., Iñigo, M., Biscarri, F., & Biscarri, J. (2014). Improving Knowledge-Based systems with statistical techniques, text mining, and neural networks for non-technical loss detection. Knowledge-Based Systems, vol. 71, 376-388.
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. Volume 19, Number 2,, 105-120.
Nagi, J., Yap, K. S., Tiong, S. K., & Mohamad, M. (2010). Nontechnical Loss Detection for Metered Customers in Power Utility Using Support Vector Machines. IEEE TRANSACTIONS ON POWER DELIVERY, VOL. 25, NO. 2,, 1162-1171.
Nagi, J., Yap, K. S., Tiong, S. K., & Mohammad, A. M. (2008). Detection of Abnormalities and Electricity Theft using Genetic Support Vector Machines. IEEE TENCON Region 10 Conference, 1-6.
Navani, J. P., Sharma, N. K., & Sapra, S. (2003). Technical and Non-Technical Losses in Power System and Its Economic Consequence in Indian Economy. International Journal of Electronics and Computer Science Engineering IJCECSE. V 1. N°2, 757-761.
Navani, J. P., Sharma, N. K., & Sapra, S. (2014). 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. V1. N°3, 396-405.
Ramos, C. C., Souza, A. N., Chiachia, G., Falcão, A. X., & Papa, J. P. (2011). A novel algorithm for feature selection using Harmony Search and its application for non-technical losses detection. Computers & Electrical Engineering 37, 886-894.
Refou, O., Alsafasfeh, Q., & Alsoud, M. (2015). Evaluation of Electric Energy Losses in Southern Governorates of Jordan Distribution Electric System. International Journal of Energy Engineering. V5. N° 2, 25-33.
Williams, G. (2011). Data Mining with Rattle and R: The Art of Excavating Data for Knowledge Discovery. New York: Springer.
Ye, N. (2014). Data Mining. Theories, Algorithms, and examples. . Boca Raton: CRC Press.
Copyright (c) 2018 Revista Facultad de Ingeniería
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
All the texts included in the Revista Facultad de Ingenieria Universidad de Antioquia -redin- are protected by copyrights. According to the law, their reproduction through any means, physical or electronic, without written consent by the Editorial Committee is forbidden. Complete texts of the articles will be fully and publically available, which means that they can be read, downloaded, copied, distributed, printed, searched for, or linked to. The opinions expressed in the published articles specifically belong to the authors and are not necessarily the same of the Editorial Committee or of the School of Engineering Management.