Identification of the characteristics incident to the detection of non-technical losses for two Colombian energy companies

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 variable...

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Fecha de publicación:
2017
Institución:
Universidad de Medellín
Repositorio:
Repositorio UDEM
Idioma:
eng
OAI Identifier:
oai:repository.udem.edu.co:11407/4267
Acceso en línea:
http://hdl.handle.net/11407/4267
Palabra clave:
Benford's Law
Cluster
Decision trees
MDS
Non-technical losses
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http://purl.org/coar/access_right/c_16ec
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oai_identifier_str oai:repository.udem.edu.co:11407/4267
network_acronym_str REPOUDEM2
network_name_str Repositorio UDEM
repository_id_str
dc.title.spa.fl_str_mv Identification of the characteristics incident to the detection of non-technical losses for two Colombian energy companies
title Identification of the characteristics incident to the detection of non-technical losses for two Colombian energy companies
spellingShingle Identification of the characteristics incident to the detection of non-technical losses for two Colombian energy companies
Benford's Law
Cluster
Decision trees
MDS
Non-technical losses
title_short Identification of the characteristics incident to the detection of non-technical losses for two Colombian energy companies
title_full Identification of the characteristics incident to the detection of non-technical losses for two Colombian energy companies
title_fullStr Identification of the characteristics incident to the detection of non-technical losses for two Colombian energy companies
title_full_unstemmed Identification of the characteristics incident to the detection of non-technical losses for two Colombian energy companies
title_sort Identification of the characteristics incident to the detection of non-technical losses for two Colombian energy companies
dc.contributor.affiliation.spa.fl_str_mv Sánchez-Zuleta, C.C., Departamento de Facultad de Ciencias Básicas, Universidad de Medellín, Carrera 87 # 30 - 65, Medellín, Colombia
Fernández-Gutiérrez, J.P., Departamento de Facultad de Ciencias Básicas, Universidad de Medellín, Carrera 87 # 30 - 65, Medellín, Colombia
Piedrahita-Escobar, C.C., Departamento de Facultad de Ciencias Básicas, Universidad de Medellín, Carrera 87 # 30 - 65, Medellín, Colombia
dc.subject.keyword.eng.fl_str_mv Benford's Law
Cluster
Decision trees
MDS
Non-technical losses
topic Benford's Law
Cluster
Decision trees
MDS
Non-technical losses
description 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.
publishDate 2017
dc.date.accessioned.none.fl_str_mv 2017-12-19T19:36:43Z
dc.date.available.none.fl_str_mv 2017-12-19T19:36:43Z
dc.date.created.none.fl_str_mv 2017
dc.type.eng.fl_str_mv Article
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dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_6501
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dc.type.driver.none.fl_str_mv info:eu-repo/semantics/article
dc.identifier.issn.none.fl_str_mv 1206230
dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/11407/4267
dc.identifier.doi.none.fl_str_mv 10.17533/udea.redin.n84a08
dc.identifier.reponame.spa.fl_str_mv reponame:Repositorio Institucional Universidad de Medellín
dc.identifier.instname.spa.fl_str_mv instname:Universidad de Medellín
identifier_str_mv 1206230
10.17533/udea.redin.n84a08
reponame:Repositorio Institucional Universidad de Medellín
instname:Universidad de Medellín
url http://hdl.handle.net/11407/4267
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.isversionof.spa.fl_str_mv https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029857355&doi=10.17533%2fudea.redin.n84a08&partnerID=40&md5=c7181a171c796e0ae37e81518f7379c1
dc.relation.ispartofes.spa.fl_str_mv Revista Facultad de Ingenieria
Revista Facultad de Ingenieria Volume 2017, Issue 84, 2017, Pages 60-71
dc.relation.references.spa.fl_str_mv (2011). Propuesta Para Remunerar Planes De Reducción De Pérdidas no Tecnicas De Energía Electrica En Sistemas De Distribución Local.
Aranha Neto, E. A. C., & Coelho, J. (2013). Probabilistic methodology for technical and non-technical losses estimation in distribution system. Electric Power Systems Research, 97, 93-99. doi:10.1016/j.epsr.2012.12.008
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. Fraud Analytics using Descriptive, Predictive, and Social Network Techniques: A Guide to Data Science for Fraud Detection.
Borg, I., & Groenen, P. (1997). Modern Multidimensional Scaling: Theory and Applications.
Borg, I., Groenen, P. J. F., & Mair, P. (2013). Applied multidimensional scaling. Applied Multidimensional Scaling.
Cox, T. F., & Cox, M. A. A. (1994). Multidimensional Scaling.
Faria, L. T., Melo, J. D., & Padilha-Feltrin, A. (2016). Spatial-temporal estimation for nontechnical losses. IEEE Transactions on Power Delivery, 31(1), 362-369. doi:10.1109/TPWRD.2015.2469135
Glauner, P. (2016). Large-Scale Detection of Non-Technical Losses in Imbalanced Data Sets.
Glauner, P., Meira, J., Valtchev, P., State, R., & Bettinger, F. (2017). The challenge of non-technical loss detection using artificial intelligence: A survey.
Guerrero, J. I., León, C., Monedero, I., 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, 71, 376-388. doi:10.1016/j.knosys.2014.08.014
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. doi:10.1109/TST.2014.6787363
Nagi, J., Yap, K. S., Tiong, S. K., Ahmed, S. K., & Mohamad, M. (2010). Nontechnical loss detection for metered customers in power utility using support vector machines. IEEE Transactions on Power Delivery, 25(2), 1162-1171. doi:10.1109/TPWRD.2009.2030890
Nagi, J., Yap, K. S., Tiong, S. K., Ahmed, S. K., & Mohammad, A. M. (2008). Detection of abnormalities and electricity theft using genetic support vector machines. Paper presented at the IEEE Region 10 Annual International Conference, Proceedings/TENCON, doi:10.1109/TENCON.2008.4766403
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, 1(3), 396-405.
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.
Ramos, C. C. O., 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 and Electrical Engineering, 37(6), 886-894. doi:10.1016/j.compeleceng.2011.09.013
Refou, O., Alsafasfeh, Q., & Alsoud, M. (2015). Evaluation of electrical energy losses in southern governorates of jordan distribution electric system. Int J Energy Eng, 5(2), 25-32.
Williams, G. (2011). Data mining with rattle and R. Data Mining with Rattle and R: The Art of Excavating Data for Knowledge Discovery.
Ye, N. (2013). Data Mining: Theories, Algorithms, and Examples.
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_16ec
rights_invalid_str_mv http://purl.org/coar/access_right/c_16ec
dc.publisher.spa.fl_str_mv Universidad de Antioquia
dc.publisher.faculty.spa.fl_str_mv Facultad de Ciencias Básicas
dc.source.spa.fl_str_mv Scopus
institution Universidad de Medellín
repository.name.fl_str_mv Repositorio Institucional Universidad de Medellin
repository.mail.fl_str_mv repositorio@udem.edu.co
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spelling 2017-12-19T19:36:43Z2017-12-19T19:36:43Z20171206230http://hdl.handle.net/11407/426710.17533/udea.redin.n84a08reponame:Repositorio Institucional Universidad de Medellíninstname:Universidad de MedellínThe 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.engUniversidad de AntioquiaFacultad de Ciencias Básicashttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85029857355&doi=10.17533%2fudea.redin.n84a08&partnerID=40&md5=c7181a171c796e0ae37e81518f7379c1Revista Facultad de IngenieriaRevista Facultad de Ingenieria Volume 2017, Issue 84, 2017, Pages 60-71(2011). Propuesta Para Remunerar Planes De Reducción De Pérdidas no Tecnicas De Energía Electrica En Sistemas De Distribución Local.Aranha Neto, E. A. C., & Coelho, J. (2013). Probabilistic methodology for technical and non-technical losses estimation in distribution system. Electric Power Systems Research, 97, 93-99. doi:10.1016/j.epsr.2012.12.008Baesens, B., Van Vlasselaer, V., & Verbeke, W. (2015). Fraud analytics using descriptive, predictive, and social network techniques: A guide to data science for fraud detection. Fraud Analytics using Descriptive, Predictive, and Social Network Techniques: A Guide to Data Science for Fraud Detection.Borg, I., & Groenen, P. (1997). Modern Multidimensional Scaling: Theory and Applications.Borg, I., Groenen, P. J. F., & Mair, P. (2013). Applied multidimensional scaling. Applied Multidimensional Scaling.Cox, T. F., & Cox, M. A. A. (1994). Multidimensional Scaling.Faria, L. T., Melo, J. D., & Padilha-Feltrin, A. (2016). Spatial-temporal estimation for nontechnical losses. IEEE Transactions on Power Delivery, 31(1), 362-369. doi:10.1109/TPWRD.2015.2469135Glauner, P. (2016). Large-Scale Detection of Non-Technical Losses in Imbalanced Data Sets.Glauner, P., Meira, J., Valtchev, P., State, R., & Bettinger, F. (2017). The challenge of non-technical loss detection using artificial intelligence: A survey.Guerrero, J. I., León, C., Monedero, I., 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, 71, 376-388. doi:10.1016/j.knosys.2014.08.014Jiang, 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. doi:10.1109/TST.2014.6787363Nagi, J., Yap, K. S., Tiong, S. K., Ahmed, S. K., & Mohamad, M. (2010). Nontechnical loss detection for metered customers in power utility using support vector machines. IEEE Transactions on Power Delivery, 25(2), 1162-1171. doi:10.1109/TPWRD.2009.2030890Nagi, J., Yap, K. S., Tiong, S. K., Ahmed, S. K., & Mohammad, A. M. (2008). Detection of abnormalities and electricity theft using genetic support vector machines. Paper presented at the IEEE Region 10 Annual International Conference, Proceedings/TENCON, doi:10.1109/TENCON.2008.4766403Navani, 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, 1(3), 396-405.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.Ramos, C. C. O., 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 and Electrical Engineering, 37(6), 886-894. doi:10.1016/j.compeleceng.2011.09.013Refou, O., Alsafasfeh, Q., & Alsoud, M. (2015). Evaluation of electrical energy losses in southern governorates of jordan distribution electric system. Int J Energy Eng, 5(2), 25-32.Williams, G. (2011). Data mining with rattle and R. Data Mining with Rattle and R: The Art of Excavating Data for Knowledge Discovery.Ye, N. (2013). Data Mining: Theories, Algorithms, and Examples.ScopusIdentification of the characteristics incident to the detection of non-technical losses for two Colombian energy companiesArticleinfo:eu-repo/semantics/articlehttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Sánchez-Zuleta, C.C., Departamento de Facultad de Ciencias Básicas, Universidad de Medellín, Carrera 87 # 30 - 65, Medellín, ColombiaFernández-Gutiérrez, J.P., Departamento de Facultad de Ciencias Básicas, Universidad de Medellín, Carrera 87 # 30 - 65, Medellín, ColombiaPiedrahita-Escobar, C.C., Departamento de Facultad de Ciencias Básicas, Universidad de Medellín, Carrera 87 # 30 - 65, Medellín, ColombiaSánchez-Zuleta C.C.Fernández-Gutiérrez J.P.Piedrahita-Escobar C.C.Departamento de Facultad de Ciencias Básicas, Universidad de Medellín, Carrera 87 # 30 - 65, Medellín, ColombiaBenford's LawClusterDecision treesMDSNon-technical lossesThe 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.http://purl.org/coar/access_right/c_16ec11407/4267oai:repository.udem.edu.co:11407/42672020-05-27 19:17:35.095Repositorio Institucional Universidad de Medellinrepositorio@udem.edu.co

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